forecasting us home prices with neural network and fuzzy methods



Ehab Saif Ghith,Moustafa Mohamed Eissa and Gurvinder S. Virk Induction Motor Speed Control Technique Using Intelligent Methods Ehab Saif Ghith,Moustafa Mohamed Eissa and Gurvinder S. Virk Induction Motor Speed Control Technique Using Intelligent Methods Новинка

Ehab Saif Ghith,Moustafa Mohamed Eissa and Gurvinder S. Virk Induction Motor Speed Control Technique Using Intelligent Methods

6190 руб.
Book relates to the speed control of an induction motor introduced intelligent methods such as Fuzzy Logic Control (FLC), Artificial Neural Networks (ANN), Adaptive Neural Fuzzy Inference System (ANFIS) and Optimization Techniques such as Genetic Algorithm (GA), Sequential Quadratic Programming (SQP) and Particle Swarm Optimization Algorithms(PSO).The results showed that the PSO-PI controller can perform with an efficient way for searching for the optimal PI controller. Comparison study among fuzzy logic, neural network, Adaptive Neural Fuzzy Inference System , genetic algorithm, sequential quadratic programming and particle swarm optimization controllers are performed. These methods can improve the dynamic performance of the system in a better way.The PI-PSO controller is the best method based on integrated of time weight absolute error (ITAE)criteria which presented satisfactory performances and possesses good robustness (no overshoot, minimal rise time, steady state error almost to zero value). A comparison study has been done between selected methods and some other technique which showed that the proposed controller has setting time less than other methods by 40%.
Hojjat Adeli Computational Intelligence. Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing Hojjat Adeli Computational Intelligence. Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing Новинка

Hojjat Adeli Computational Intelligence. Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing

11232.84 руб.
Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB® exercises and applications in each chapter Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems Considers real world problems in the domain of systems modelling, control and optimization Contains a foreword written by Lotfi Zadeh Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.
Derong Liu Fundamentals of Computational Intelligence. Neural Networks, Fuzzy Systems, and Evolutionary Computation Derong Liu Fundamentals of Computational Intelligence. Neural Networks, Fuzzy Systems, and Evolutionary Computation Новинка

Derong Liu Fundamentals of Computational Intelligence. Neural Networks, Fuzzy Systems, and Evolutionary Computation

9296.14 руб.
Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals Examines evolutionary optimization, evolutionary learning and problem solving, and collective intelligence Includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems Fundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.
Reinhard Viertl Statistical Methods for Fuzzy Data Reinhard Viertl Statistical Methods for Fuzzy Data Новинка

Reinhard Viertl Statistical Methods for Fuzzy Data

9219.83 руб.
Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be best described by using fuzzy numbers and fuzzy vectors respectively. Statistical analysis methods have to be adapted for the analysis of fuzzy data. In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy measurement results. Furthermore, statistical methods are then generalized to the analysis of fuzzy data and fuzzy a-priori information. Key Features: Provides basic methods for the mathematical description of fuzzy data, as well as statistical methods that can be used to analyze fuzzy data. Describes methods of increasing importance with applications in areas such as environmental statistics and social science. Complements the theory with exercises and solutions and is illustrated throughout with diagrams and examples. Explores areas such quantitative description of data uncertainty and mathematical description of fuzzy data. This work is aimed at statisticians working with fuzzy logic, engineering statisticians, finance researchers, and environmental statisticians. It is written for readers who are familiar with elementary stochastic models and basic statistical methods.
Antonios Alexandridis K. Wavelet Neural Networks. With Applications in Financial Engineering, Chaos, and Classification Antonios Alexandridis K. Wavelet Neural Networks. With Applications in Financial Engineering, Chaos, and Classification Новинка

Antonios Alexandridis K. Wavelet Neural Networks. With Applications in Financial Engineering, Chaos, and Classification

7979.04 руб.
A step-by-step introduction to modeling, training, and forecasting using wavelet networks Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification presents the statistical model identification framework that is needed to successfully apply wavelet networks as well as extensive comparisons of alternate methods. Providing a concise and rigorous treatment for constructing optimal wavelet networks, the book links mathematical aspects of wavelet network construction to statistical modeling and forecasting applications in areas such as finance, chaos, and classification. The authors ensure that readers obtain a complete understanding of model identification by providing in-depth coverage of both model selection and variable significance testing. Featuring an accessible approach with introductory coverage of the basic principles of wavelet analysis, Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification also includes: • Methods that can be easily implemented or adapted by researchers, academics, and professionals in identification and modeling for complex nonlinear systems and artificial intelligence • Multiple examples and thoroughly explained procedures with numerous applications ranging from financial modeling and financial engineering, time series prediction and construction of confidence and prediction intervals, and classification and chaotic time series prediction • An extensive introduction to neural networks that begins with regression models and builds to more complex frameworks • Coverage of both the variable selection algorithm and the model selection algorithm for wavelet networks in addition to methods for constructing confidence and prediction intervals Ideal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial intelligence, advanced data analysis, time series, and forecasting in financial engineering, the book is also useful as a supplement for courses in informatics, identification and modeling for complex nonlinear systems, and computational finance. In addition, the book serves as a valuable reference for researchers and practitioners in the fields of mathematical modeling, engineering, artificial intelligence, decision science, neural networks, and finance and economics.
Dr. Mohamed E. El-Hawary Advances in Electric Power and Energy Systems. Load and Price Forecasting Dr. Mohamed E. El-Hawary Advances in Electric Power and Energy Systems. Load and Price Forecasting Новинка

Dr. Mohamed E. El-Hawary Advances in Electric Power and Energy Systems. Load and Price Forecasting

9683.48 руб.
A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial arenas. Short-run forecasting of electricity prices has become necessary for power generation unit schedule, since it is the basis of every maximization strategy. This book fills a gap in the literature on this increasingly important topic. Following an introductory chapter offering background information necessary for a full understanding of the forecasting issues covered, this book: Introduces advanced methods of time series forecasting, as well as neural networks Provides in-depth coverage of state-of-the-art power system load forecasting and electricity price forecasting Addresses river flow forecasting based on autonomous neural network models Deals with price forecasting in a competitive market Includes estimation of post-storm restoration times for electric power distribution systems Features contributions from world-renowned experts sharing their insights and expertise in a series of self-contained chapters Advances in Electric Power and Energy Systems is a valuable resource for practicing engineers, regulators, planners, and consultants working in or concerned with the electric power industry. It is also a must read for senior undergraduates, graduate students, and researchers involved in power system planning and operation.
Sudha Thatiparthi,Balasiddamuni Pagadala and Sarojamma B. Some Aspects of Applied Forecasting Methods Sudha Thatiparthi,Balasiddamuni Pagadala and Sarojamma B. Some Aspects of Applied Forecasting Methods Новинка

Sudha Thatiparthi,Balasiddamuni Pagadala and Sarojamma B. Some Aspects of Applied Forecasting Methods

5787 руб.
This book has brought out inferential methods to forecasting with linear statistical and time series models, the various forecasting methods existing in the literature have been briefly reviewed with inferential problems on them. In view of the importance of forecasting is empirical research, some new procedures for applied forecasting have been developed.Here, these techniques are developed by using Internally Studentized Residuals. Further, a modified Box-Jenkins methodology has been presented for auto Integrated Moving average model ARIMA(p,d,q) based on Internally Studentized Residuals. Under Diagnostic checking, a modified L Jung and Box statistic for testing the residuals has been proposed. The forecasts to be obtained from this methodology may be used as benchmark to compare with forecasts to be yielded by other forecasting techniques.
Nawaz Aamir Nelder Mead Trained Neural Networks For Short Term Load Forecasting Nawaz Aamir Nelder Mead Trained Neural Networks For Short Term Load Forecasting Новинка

Nawaz Aamir Nelder Mead Trained Neural Networks For Short Term Load Forecasting

7277 руб.
This book proposes a new optimization algorithm for solving short term load forecasting problem. Globalized Nelder Mead is used for training of Artificial Neural Networks. Nelder Mead is fast optimization algorithm with no gradient calculation. The weights of Neural Networks are tuned with the help of Nelder Mead algorithm. To find proficiency of this algorithm, Australian Energy Market Operator (AEMO) data and California data are taken for testing. Results show that proposed algorithm outclasses other techniques in literature.
Majid Amirfakhrian Some Approximation Methods in Fuzzy Logic Majid Amirfakhrian Some Approximation Methods in Fuzzy Logic Новинка

Majid Amirfakhrian Some Approximation Methods in Fuzzy Logic

9989 руб.
In this book we define some new methods to approximate a fuzzy function by fuzzy polynomials. Also by introducing some new distances we define the nearest approximations of a fuzzy number. ‎In his book‎, the ‎definition of fuzzy linear programming with fuzzy variables and a‎ ‎method for solving it according to a special class of ranking which is used ‎to find the approximating polynomials as well as‎ ‎definition of the problem of approximation‎, ‎‎are discussed‎. Then the approximation problem on triangular fuzzy numbers ‎leads us to an approximating polynomial name eϕ-approximation‎ ‎and on the set of all fuzzy numbers‎, ‎the approximation problem gives ‎us the D-approximation and we present a method to find it‎, ‎ also ‎the universal and SAF-approximations which are special cases of D-approximation are found in this book‎. ‎Also two best ‎approximations of a triangular valued fuzzy function on a set of‎ ‎points are defined and are computed‎. ‎Furthermore a chapter contains an idea for ‎computing the nearest approximation of a fuzzy number out of a‎ ‎particular subset of all fuzzy numbers.
Kostas Nikolopoulos I. Forecasting With The Theta Method. Theory and Applications Kostas Nikolopoulos I. Forecasting With The Theta Method. Theory and Applications Новинка

Kostas Nikolopoulos I. Forecasting With The Theta Method. Theory and Applications

8587.68 руб.
The first book to be published on the Theta method, outlining under what conditions the method outperforms other forecasting methods This book is the first to detail the Theta method of forecasting – one of the most difficult-to-beat forecasting benchmarks, which topped the biggest forecasting competition in the world in 2000: the M3 competition. Written by two of the leading experts in the forecasting field, it illuminates the exact replication of the method and under what conditions the method outperforms other forecasting methods. Recent developments such as multivariate models are also included, as are a series of practical applications in finance, economics, and healthcare. The book also offers practical tools in MS Excel and guidance, as well as provisional access, for the use of R source code and respective packages. Forecasting with the Theta Method: Theory and Applications includes three main parts. The first part, titled Theory, Methods, Models & Applications details the new theory about the method. The second part, Applications & Performance in Forecasting Competitions, describes empirical results and simulations on the method. The last part roadmaps future research and also include contributions from another leading scholar of the method – Dr. Fotios Petropoulos. First ever book to be published on the Theta Method Explores new theory and exact conditions under which methods would outperform most forecasting benchmarks Clearly written with practical applications Employs R – open source code with all included implementations Forecasting with the Theta Method: Theory and Applications is a valuable tool for both academics and practitioners involved in forecasting and respective software development.
Haider Raza Fuzzy Spiking Neural Networks Haider Raza Fuzzy Spiking Neural Networks Новинка

Haider Raza Fuzzy Spiking Neural Networks

3752 руб.
Master's Thesis from the year 2011 in the subject Engineering - Computer Engineering, grade: 8.84, Manav Rachna International University, course: Master of Technology (M.Tech), language: English, abstract: This dissertation presents an introductory knowledge to computational neuroscience and major emphasize on the branch of computational neuroscience called Spiking Neural Networks (SNNs). SNNs are also called the third generation neural networks. It has become now a major field of Soft Computing. In this we talk about the temporal characteristics' of neuron and studied the dynamics of it. We have presented SNNs architecture with fuzzy reasoning capability. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train frequencies and behave in a similar manner as fuzzy membership functions. The network of SNNs consists of three layers that is input, hidden and output layer. The topology of this network is based on Radial basis Network, which can be regarded as universal approximators. The input layer receives the input in the form of frequency which produces the spikes through linear encoding. There is another method of encoding called Poisson encoding; this encoding is used where the data is large. The hidden layer use Receptive Field (RF) to process the input and thus it is frequency selective. The output layer is only responsible for learning. The learning is based on local learning. The XOR classific...
Moazzam Hossain Intrusion Detection with Artificial Neural Networks Moazzam Hossain Intrusion Detection with Artificial Neural Networks Новинка

Moazzam Hossain Intrusion Detection with Artificial Neural Networks

9877 руб.
Intrusion detection system is a detection mechanism that detects unauthorized, malicious presents in the computer systems. The purpose of this book is to design, implement and evaluate an anomaly based network intrusion detection system. The System learns about the normal users' behavior and finds the anomalies by matching with this normal behavior. A special type of neural network called backpropagation neural network is used for learning normal users' behavior. The network traffic that only contains information of normal users is presented with the neural network for learning about the normal users' behavior. The system performance has been tested by using a simulated computer network. The neural network is trained with huge,not so huge and small amount of data. The detection capability of the system has been tested with huge and small amount of data. It is seen from the performance analysis that the system performs well when trained with small amount of data. An overall detection rate of 98% has been achieved for both known and unknown attacks. Moreover, the system can detect 100% normal user.
Md. Safiur Rahman Mahdi, A.B.M. Zunaid Haque, S.M. Al Mamun Protein Secondary Structure Prediction Md. Safiur Rahman Mahdi, A.B.M. Zunaid Haque, S.M. Al Mamun Protein Secondary Structure Prediction Новинка

Md. Safiur Rahman Mahdi, A.B.M. Zunaid Haque, S.M. Al Mamun Protein Secondary Structure Prediction

8377 руб.
Protein secondary structure prediction is a very hot topic in bioinformatics. Predicting protein secondary structure means to find out the portions that contain Helix and Sheet in protein sequence. There are several methods for predicting protein secondary structure. The methods like Genetic Algorithm, Hidden Markov Model and different kinds of Neural Networks are there. Genetic Algorithm mostly deals with protein tertiary structure and sequence alignment, for Hidden Markov Model the accuracy is not good and Neural Network is the most successful for predicting protein secondary structure. So, we used the method named "Feed Forward Neural Network" and implemented it with JOONE (Java Object Oriented Neural Engine) editor. At first we have classified the 20 protein according to their structure, size and hydrophobic manner. Then we have modeled a new architecture in feed forward network and used those classified proteins as input. Our achieved accuracy of helix prediction is 71% and sheet prediction is 65%. The result shows the improvement over previous works done in this regard. We hope that our work will be a future directive in this arena.
Antonin Dvorak Insight into Fuzzy Modeling Antonin Dvorak Insight into Fuzzy Modeling Новинка

Antonin Dvorak Insight into Fuzzy Modeling

9296.14 руб.
Provides a unique and methodologically consistent treatment of various areas of fuzzy modeling and includes the results of mathematical fuzzy logic and linguistics This book is the result of almost thirty years of research on fuzzy modeling. It provides a unique view of both the theory and various types of applications. The book is divided into two parts. The first part contains an extensive presentation of the theory of fuzzy modeling. The second part presents selected applications in three important areas: control and decision-making, image processing, and time series analysis and forecasting. The authors address the consistent and appropriate treatment of the notions of fuzzy sets and fuzzy logic and their applications. They provide two complementary views of the methodology, which is based on fuzzy IF-THEN rules. The first, more traditional method involves fuzzy approximation and the theory of fuzzy relations. The second method is based on a combination of formal fuzzy logic and linguistics. A very important topic covered for the first time in book form is the fuzzy transform (F-transform). Applications of this theory are described in separate chapters and include image processing and time series analysis and forecasting. All of the mentioned components make this book of interest to students and researchers of fuzzy modeling as well as to practitioners in industry. Features: Provides a foundation of fuzzy modeling and proposes a thorough description of fuzzy modeling methodology Emphasizes fuzzy modeling based on results in linguistics and formal logic Includes chapters on natural language and approximate reasoning, fuzzy control and fuzzy decision-making, and image processing using the F-transform Discusses fuzzy IF-THEN rules for approximating functions, fuzzy cluster analysis, and time series forecasting Insight into Fuzzy Modeling is a reference for researchers in the fields of soft computing and fuzzy logic as well as undergraduate, master and Ph.D. students. Vilém Novák, D.Sc. is Full Professor and Director of the Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic. Irina Perfilieva, Ph.D. is Full Professor, Senior Scientist, and Head of the Department of Theoretical Research at the Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic. Antonín Dvorák, Ph.D. is Associate Professor, and Senior Scientist at the Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic.
Stefan Vogt A Design and Development Method for Artificial Neural Network Projects Stefan Vogt A Design and Development Method for Artificial Neural Network Projects Новинка

Stefan Vogt A Design and Development Method for Artificial Neural Network Projects

4639 руб.
Inhaltsangabe:Abstract: In the 1980s research efforts and successes made artificial neural networks popular. Since the 1990s engineers have been using this foundation for problem solving. But artifiial neural network solutions for "real-world" problems are sometimes hard to find because of the complexity of the domain and because of the vast number of design attributes the engineer has to deal with. This thesis provides a structured overview of attributes in the design process of artificial neural networks and reviews technical process models. Current development methods for artificial neural networks are then reviewed and critiqued. The thesis concludes with a new design and development method for artificial neural networks. Inhaltsverzeichnis:Table of Contents: List of figuresx List of tablesxi Introduction1 1.Design attributes in ANN3 1.1ANN models4 1.1.1Node level7 1.1.2Network level9 1.1.3Training level9 1.2Data and data representation10 1.3Global system design12 1.4Hardware and software implementation13 1.5Characteristics of ANNs15 1.5.1Advantages of ANNs15 1.5.2Limitations and concerns16 2.Technical process models and engineering methods18 2.1Why use an engineering method?18 2.2Evolutionary model of engineering discipline20 2.3Overview of technical process models22 2.3.1Taxonomy of technical process models24 2.3.2Prototyping25 2.3.3Incremental method26 2.3.4Strict contractual approach26 2.3.5Deciding on process models and methods26 2.3.6Examples of process mode...
Snehashish Chakraverty Fuzzy Arbitrary Order System. Fuzzy Fractional Differential Equations and Applications Snehashish Chakraverty Fuzzy Arbitrary Order System. Fuzzy Fractional Differential Equations and Applications Новинка

Snehashish Chakraverty Fuzzy Arbitrary Order System. Fuzzy Fractional Differential Equations and Applications

7743.11 руб.
Presents a systematic treatment of fuzzy fractional differential equations as well as newly developed computational methods to model uncertain physical problems Complete with comprehensive results and solutions, Fuzzy Arbitrary Order System: Fuzzy Fractional Differential Equations and Applications details newly developed methods of fuzzy computational techniquesneeded to model solve uncertainty. Fuzzy differential equations are solved via various analytical andnumerical methodologies, and this book presents their importance for problem solving, prototypeengineering design, and systems testing in uncertain environments. In recent years, modeling of differential equations for arbitrary and fractional order systems has been increasing in its applicability, and as such, the authors feature examples from a variety of disciplines to illustrate the practicality and importance of the methods within physics, applied mathematics, engineering, and chemistry, to name a few. The fundamentals of fractional differential equations and the basic preliminaries of fuzzy fractional differential equations are first introduced, followed by numerical solutions, comparisons of various methods, and simulated results. In addition, fuzzy ordinary, partial, linear, and nonlinear fractional differential equations are addressed to solve uncertainty in physical systems. In addition, this book features: Basic preliminaries of fuzzy set theory, an introduction of fuzzy arbitrary order differential equations, and various analytical and numerical procedures for solving associated problems Coverage on a variety of fuzzy fractional differential equations including structural, diffusion, and chemical problems as well as heat equations and biomathematical applications Discussions on how to model physical problems in terms of nonprobabilistic methods and provides systematic coverage of fuzzy fractional differential equations and its applications Uncertainties in systems and processes with a fuzzy concept Fuzzy Arbitrary Order System: Fuzzy Fractional Differential Equations and Applications is an ideal resource for practitioners, researchers, and academicians in applied mathematics, physics, biology, engineering, computer science, and chemistry who need to model uncertain physical phenomena and problems. The book is appropriate for graduate-level courses on fractional differential equations for students majoring in applied mathematics, engineering, physics, and computer science.
Olaf Wandel Robot Control using an Artificial Neural Network Olaf Wandel Robot Control using an Artificial Neural Network Новинка

Olaf Wandel Robot Control using an Artificial Neural Network

4714 руб.
Inhaltsangabe:Abstract: The aim of the project was to control three joints of an industrial robot in terms of its position, velocity and acceleration. The work considered the necessary hardware, principles of neural networks and controlling techniques. The hardware comprised of a robot with three DC-motors and three optical position encoders, a personal computer with a D/A card for voltage output to the robot and two D/D cards. One D/D card for receiving values from the optical encoders and one for timing. The basics of artificial neural network type perceptrons were described. The special features bias, output feedback, momentum term, adjustment of momentum factor and adjustment of learning rate for this artificial neural network type were considered. An introduction to learning and control structures using artificial neural networks were given. These were controller copying, direct modelling, direct inverse modelling, control with a model and an inverse model, forward and inverse modelling, control action feedback error learning, feedback error learning, learning and control using the plant’s Jacobian. The conversion of two learning and control structures, direct inverse modelling and control action feedback error learning, was implemented in software using „MS QuickBASIC 4.5“. One joint was controlled with a direct inverse model. One joint and all joints together were controlled with control action feedback error learning. The results of experiments with these learning and...
Hassan Abdelbary Forecast Stock Index using Neural Networks and Evolutionary Computing Hassan Abdelbary Forecast Stock Index using Neural Networks and Evolutionary Computing Новинка

Hassan Abdelbary Forecast Stock Index using Neural Networks and Evolutionary Computing

3944 руб.
Forecasting price index is an important problem in financial markets. In the past decades the prediction of stock index has played a vital role in the financial situation of several companies which have stocks in the market. In the past this prediction process was simple and easy for several reasons: the behavior of the stocks was known and not complicated beside the existence of a number of experts in this field. Several techniques are used to predict and model the stock market behavior and try to increase the accuracy of prediction. Neural networks have several characteristics which make them good models to predict the complex behavior of stock index and increase the accuracy of the prediction. Combining neural networks with evolutionary computational methods like Genetic Algorithms and Simulated Annealing can give better results in learning neural networks specially for problem of forecasting stock index.
Joish Bosco, Fateh Khan Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network Joish Bosco, Fateh Khan Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network Новинка

Joish Bosco, Fateh Khan Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network

5214 руб.
Project Report from the year 2018 in the subject Computer Science - Technical Computer Science, , course: Computer Science, language: English, abstract: Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environm...
Manjubala Bisi Artificial Neural Network Applications for Software Reliability Prediction Manjubala Bisi Artificial Neural Network Applications for Software Reliability Prediction Новинка

Manjubala Bisi Artificial Neural Network Applications for Software Reliability Prediction

15106.96 руб.
Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process is presented as well. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.
Navjot Kaur Neural Stem Cell Assays Navjot Kaur Neural Stem Cell Assays Новинка

Navjot Kaur Neural Stem Cell Assays

12003.84 руб.
Neural stem cells offer a valuable model system for delineating the cellular and developmental processes in normal and diseased states of the central nervous system. In particular, neural stem cells have huge potential in regenerative medicine, owing to their expansion capability in culture and the ability to differentiate into multiple sub-neural lineages. Neural Stem Cell Assays provides a detailed and comprehensive review of the basic methods for neural stem cell cultures. Including an overview of progress in the field over the past decade, Neural Stem Cell Assays is a one-stop reference for consistent methods and reliable tools that span the entire assay work flow, from isolation or generation of neural stem cells to characterization, manipulation and final application of neural stem cells in disease paradigms such as Parkinson's disease, multiple sclerosis and amyotrophic lateral sclerosis. An excellent source of information for academic, pharmaceutical and biotechnology researchers who are new to the neural stem cell field, Neural Stem Cell Assays is an invaluable to experienced users who wish to integrate newly developed tools and technologies into their workflow. The book also covers important course material for students at the undergraduate and graduate level who are learning the basics of neural stem cell cultures, and differentiation to sub-neural lineages.
Tai Doan Convolutional Neural Network in classifying scanned documents Tai Doan Convolutional Neural Network in classifying scanned documents Новинка

Tai Doan Convolutional Neural Network in classifying scanned documents

1852 руб.
Internship Report from the year 2016 in the subject Computer Science - Applied, University of Science and Technology of Hanoi, course: Internship, language: English, abstract: In this project, I created and augmented a dataset from a number of given images to train and test convolutional neural network which is used to classify five classes of images of scanned documents. In order to generate the dataset, some image processing techniques were applied such as sliding-window, rotating, flipping and pyramid-sizing. The result of this phase is a set of images having same size 244x224x3. These images after being labeled were divided into three dataset for training, validating and testing the network. The network is a simple convolution neural network which is also called LeNet. It has three convolutional layers and one fully connected layer. After being trained and validated, the best state of the network was pointed out and tested on the testing dataset and some real images. The result showed that the LeNet was able to classify images of documents in a pretty high accuracy. At the end of the project, I modified the network and discussed the affect that those changes had on the network with the purpose of creating another similar network which can perform better than the original one. The result proved that it worked a little better than its original version.
Sue Plumley Home Networking Bible Sue Plumley Home Networking Bible Новинка

Sue Plumley Home Networking Bible

2384.81 руб.
Everything you need to know to set up a home network Is a home network for you? This comprehensive guide covers everything from deciding what type of network meets your needs to setting up the hardware and software, connecting different operating systems, installing the necessary applications, managing the network, and even adding home entertainment devices. Fully updated with new material on all the latest systems and methods, it's just what you need to set up your network and keep it running safely and successfully. Inside, you'll find complete coverage of home networking * Compare the advantages and disadvantages of wired and wireless networks * Understand how to choose between workgroup and client/server networking * Learn how to install and set up cables and routers and how to install and configure networking software * Share files, printers, and a single Internet connection * Back up files and secure your network * Set up your own home intranet and understand the technologies involved in creating a Web page * Manage your network and learn to use tools for locating and repairing problems * Expand your home network to include your digital camera, scanner, TV, sound system, and even game consoles * Explore SmartHome technology that allows you to automate various household functions * Investigate how your network can enable tele-commuting and other remote access capabilities
Kafayat Adeoye Fingerprint Recognition System Using Artifical Neural Network Kafayat Adeoye Fingerprint Recognition System Using Artifical Neural Network Новинка

Kafayat Adeoye Fingerprint Recognition System Using Artifical Neural Network

5252 руб.
Bachelor Thesis from the year 2017 in the subject Engineering - Computer Engineering, grade: First Class, University of Portsmouth, language: English, abstract: This project presents a fingerprint recognition system using neural network. To establish an objective assessment of the proposed neural network algorithm, fingerprint images from National institute of standards and technology (NIST) database were used. Image processing operations were carried out on the fingerprints prior to extracting the minutiae which are set as input into the network for verification or identification of a person. However, these processes are crucial to the performance of the neural network.Back-propagation neural network algorithm called Scaled Conjugate Gradient is used to train the network. The aim of this project is to implement a faster and reliable fingerprint minutiae matching algorithm and the Matlab experimental results show that the network has achieved an excellent performance in pattern recognition. Furthermore, the overall error rate is very minimal and the network generates 93.2% of accuracy for the fingerprint recognition system.
Timothy Ross J. Fuzzy Logic with Engineering Applications Timothy Ross J. Fuzzy Logic with Engineering Applications Новинка

Timothy Ross J. Fuzzy Logic with Engineering Applications

5979.87 руб.
Fuzzy Logic with Engineering Applications, Fourth Edition Timothy J. Ross, University of New Mexico, USA The latest update on this popular textbook The importance of concepts and methods based on fuzzy logic and fuzzy set theory has been rapidly growing since the early 1990s and all the indications are that this trend will continue in the foreseeable future. Fuzzy Logic with Engineering Applications, Fourth Edition is a new edition of the popular textbook with 15% of new and updated material. Updates have been made to most of the chapters and each chapter now includes new end-of-chapter problems. Key features: New edition of the popular textbook with 15% of new and updated material. Includes new examples and end-of-chapter problems. Has been made more concise with the removal of out of date material. Covers applications of fuzzy logic to engineering and science. Accompanied by a website hosting a solutions manual and software. The book is essential reading for graduates and senior undergraduate students in civil, chemical, mechanical and electrical engineering as wells as researchers and practitioners working with fuzzy logic in industry.
Michael Gilliland Business Forecasting. Practical Problems and Solutions Michael Gilliland Business Forecasting. Practical Problems and Solutions Новинка

Michael Gilliland Business Forecasting. Practical Problems and Solutions

3309.84 руб.
A comprehensive collection of the field's most provocative, influential new work Business Forecasting compiles some of the field's important and influential literature into a single, comprehensive reference for forecast modeling and process improvement. It is packed with provocative ideas from forecasting researchers and practitioners, on topics including accuracy metrics, benchmarking, modeling of problem data, and overcoming dysfunctional behaviors. Its coverage includes often-overlooked issues at the forefront of research, such as uncertainty, randomness, and forecastability, as well as emerging areas like data mining for forecasting. The articles present critical analysis of current practices and consideration of new ideas. With a mix of formal, rigorous pieces and brief introductory chapters, the book provides practitioners with a comprehensive examination of the current state of the business forecasting field. Forecasting performance is ultimately limited by the 'forecastability' of the data. Yet failing to recognize this, many organizations continue to squander resources pursuing unachievable levels of accuracy. This book provides a wealth of ideas for improving all aspects of the process, including the avoidance of wasted efforts that fail to improve (or even harm) forecast accuracy. Analyzes the most prominent issues in business forecasting Investigates emerging approaches and new methods of analysis Combines forecasts to improve accuracy Utilizes Forecast Value Added to identify process inefficiency The business environment is evolving, and forecasting methods must evolve alongside it. This compilation delivers an array of new tools and research that can enable more efficient processes and more accurate results. Business Forecasting provides an expert's-eye view of the field's latest developments to help you achieve your desired business outcomes.
Akira Hirose Complex-Valued Neural Networks. Advances and Applications Akira Hirose Complex-Valued Neural Networks. Advances and Applications Новинка

Akira Hirose Complex-Valued Neural Networks. Advances and Applications

10575.57 руб.
Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and superconducting waves. This fact is a critical advantage in practical applications in diverse fields of engineering, where signals are routinely analyzed and processed in time/space, frequency, and phase domains. Complex-Valued Neural Networks: Advances and Applications covers cutting-edge topics and applications surrounding this timely subject. Demonstrating advanced theories with a wide range of applications, including communication systems, image processing systems, and brain-computer interfaces, this text offers comprehensive coverage of: Conventional complex-valued neural networks Quaternionic neural networks Clifford-algebraic neural networks Presented by international experts in the field, Complex-Valued Neural Networks: Advances and Applications is ideal for advanced-level computational intelligence theorists, electromagnetic theorists, and mathematicians interested in computational intelligence, artificial intelligence, machine learning theories, and algorithms.
Etienne Jungbluth Is the Value of the US Dollar Driving Oil Prices. Etienne Jungbluth Is the Value of the US Dollar Driving Oil Prices. Новинка

Etienne Jungbluth Is the Value of the US Dollar Driving Oil Prices.

3452 руб.
Master's Thesis from the year 2012 in the subject Economics - Monetary theory and policy, grade: 80, University of Edinburgh, language: English, abstract: As an invoice currency the US dollar is frequently cited to cause changes in real oil prices through a demand and supply channel. The aim of this paper is to shed more light on whether the US dollar is driving oil prices. In order to do so, we incorporate the bivariate relationship in a broader demand and supply framework in which real oil prices are determined by demand and supply factors. We find cointegration among the variables involved and that the value of the US dollar is a significant part in the long-run relationship. The causal links within the long-run relationship are examined using the procedure suggested by Toda & Yamamoto (1995) to test for Granger causality in the presence of I(1) variables. The results show that no causal effect of the US dollar on oil prices can be found. This contradicts the view that the US dollar is driving real oil prices. There is even evidence that none of the fundamentals is causing real oil prices. Moreover, real oil prices are found to have an indirect causal effect on the US dollar. This contradicts standard models such as the Krugman (1983) model which suggest a direct link.
Н. З. Емельянова Simulation modeling and fuzzy logic in real-time decision-making of airport services Н. З. Емельянова Simulation modeling and fuzzy logic in real-time decision-making of airport services Новинка

Н. З. Емельянова Simulation modeling and fuzzy logic in real-time decision-making of airport services

152 руб.
Decision making by the aircrafts services of the international airport, which provides for intensive traffic of aircraft and their ground handling, becomes a very topical issue. If earlier it was believed that the intensity is provided only by the number of runways, nowadays a large accumulation of aircraft on the airport platform-field creates equally complex difficulties in comparison with aircraft take-offs and landings. Solving such problems with the use of «crisp methods» of queuing theory gives little. This article deals with modern «fuzzy methods» based on simulation modeling and fuzzy logic.
Sarah Watt Economic and Business Forecasting. Analyzing and Interpreting Econometric Results Sarah Watt Economic and Business Forecasting. Analyzing and Interpreting Econometric Results Новинка

Sarah Watt Economic and Business Forecasting. Analyzing and Interpreting Econometric Results

4969.73 руб.
Discover the secrets to applying simple econometric techniques to improve forecasting Equipping analysts, practitioners, and graduate students with a statistical framework to make effective decisions based on the application of simple economic and statistical methods, Economic and Business Forecasting offers a comprehensive and practical approach to quantifying and accurate forecasting of key variables. Using simple econometric techniques, author John E. Silvia focuses on a select set of major economic and financial variables, revealing how to optimally use statistical software as a template to apply to your own variables of interest. Presents the economic and financial variables that offer unique insights into economic performance Highlights the econometric techniques that can be used to characterize variables Explores the application of SAS software, complete with simple explanations of SAS-code and output Identifies key econometric issues with practical solutions to those problems Presenting the «ten commandments» for economic and business forecasting, this book provides you with a practical forecasting framework you can use for important everyday business applications.
Mohd Ariffanan Mohd Basri Medical Image Classification and Symptoms Detection Using Neuro Fuzzy Mohd Ariffanan Mohd Basri Medical Image Classification and Symptoms Detection Using Neuro Fuzzy Новинка

Mohd Ariffanan Mohd Basri Medical Image Classification and Symptoms Detection Using Neuro Fuzzy

8777 руб.
The conventional method in medicine for brain MR images classification and tumor detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. MR images also always contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques, for instance, neural networks, fuzzy logic, neuro fuzzy have shown great potential in this field. Hence, in this project the neuro fuzzy system or ANFIS was applied for classification and detection purposes. Decision making was performed in two stages: feature extraction using the principal component analysis (PCA) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the tumors.
Kathy Ivens Home Networking For Dummies Kathy Ivens Home Networking For Dummies Новинка

Kathy Ivens Home Networking For Dummies

1748.68 руб.
Having a network in your home increases work efficiency and minimizes confusion. If you want to set up a network in your home but you’re not quite sure where to start, then Home Networking for Dummies makes it easy for you to become your household’s network administrator. Now fully updated with information on the newest technology in networking available, this quick and to-the-point walkthrough will show you how to install Web connections in your entire home, whether by wires, cables, or WiFi. This resourceful guide illustrates: Planning and installing your network The differences between Ethernet cable, phone lines, and wireless technology Configuring computer sharing Setting up and managing users Installing, managing, and troubleshooting the network printer Understanding UNC format, mapping drives, and traveling on the network Working with remote files Securing your network from viruses, spyware, and other baddies Along with the basics, this book introduces fun ways to use your network, including sharing music, keeping shopping lists, creating photo albums, setting up a family budget, and instant messaging. It also provides ways to keep your network safe for kids, such as talking to your child about the Internet, creating site filters, and ISP E-mail filtering features. With this trusty guide your home will be fully connected and you’ll be working more efficiently in no time!
Lih Chieh Png Morphological Shared-Weight Neural Network for Face Recognition Lih Chieh Png Morphological Shared-Weight Neural Network for Face Recognition Новинка

Lih Chieh Png Morphological Shared-Weight Neural Network for Face Recognition

4178 руб.
An algorithm based on morphological shared-weight neural network is introduced. Being nonlinear and translation-invariant, the MSNN can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The output is then learned by interacting with the classification process. The feature extraction and classification networks are trained together, allowing the MSNN to simultaneously learn feature extraction and classification for a face. For evaluation, we test for robustness under variations in gray levels and noise while varying the network’s configuration to optimize recognition efficiency and processing time. Results show that the MSNN performs better for grayscale image pattern classification than ordinary neural networks.
Mahmoud Jazzar Computer Network Intrusion Detection Mahmoud Jazzar Computer Network Intrusion Detection Новинка

Mahmoud Jazzar Computer Network Intrusion Detection

6190 руб.
A typical problem that arises when deploying intrusion detection sensors is their affinities of producing high rate of false alerts. Thus, it needs huge analysis efforts and time consuming odd jobs at higher levels. In this study, we have investigated an approach to anomaly intrusion detection based on causal knowledge reasoning. The approach is anomaly-based and utilizes causal knowledge inference based fuzzy cognitive maps (FCM) and self organizing maps (SOM). A set of parallel neural network classifiers (SOM) are used to do an initial recognition of the network traffic flow to detect abnormal behaviors. The FCM is incorporated to eliminate ambiguities of odd neurons and making final decisions. Based on the domain knowledge of network data the SOM/FCM combination presents quantitative and qualitative matching correspondences which in turn reduce the number of suspicious neurons i.e. reduce the number of false alerts. This method works as a unique fuzzy clustering approach and we have demonstrated its performance using DARPA 1999 network traffic data set. The method has also the flexibility of features selection for further exploration.
Pushpinder Singh Ranking Approach to Solve Linear Programming Problems with Fuzzy Sets Pushpinder Singh Ranking Approach to Solve Linear Programming Problems with Fuzzy Sets Новинка

Pushpinder Singh Ranking Approach to Solve Linear Programming Problems with Fuzzy Sets

4419 руб.
Linear programming is one of the most frequently applied operations research techniques. The classical tool for solving the linear programming problem in practice is the class of simplex algorithm which was proposed and developed by Dantzig. A lot of real world decision problems are described by linear programming models and sometimes it is necessary to formulate them with elements of imprecision or uncertainty. This imprecise nature has long been studied with the help of the probability theory. However, the probability theory might not provide the correct interpretation to solve some practical decision making problems. In these cases, the fuzzy set theory might be more helpful. In this book, the limitations and shortcomings of existing methods for solving linear programming problems with fuzzy sets are pointed out. Some new ranking approaches for the ordering of fuzzy sets and vague sets are developed and also new methods to find the unique optimal solutions of linear programming problems under fuzzy environment and vague environment are presented.
Arthur Dexter L. Monitoring and Control of Information-Poor Systems. An Approach based on Fuzzy Relational Models Arthur Dexter L. Monitoring and Control of Information-Poor Systems. An Approach based on Fuzzy Relational Models Новинка

Arthur Dexter L. Monitoring and Control of Information-Poor Systems. An Approach based on Fuzzy Relational Models

12007.52 руб.
The monitoring and control of a system whose behaviour is highly uncertain is an important and challenging practical problem. Methods of solution based on fuzzy techniques have generated considerable interest, but very little of the existing literature considers explicit ways of taking uncertainties into account. This book describes an approach to the monitoring and control of information-poor systems that is based on fuzzy relational models which generate fuzzy outputs. The first part of Monitoring and Control of Information-Poor Systems aims to clarify why design decisions must take account of the uncertainty associated with optimal choices, and to explain how a fuzzy relational model can be used to generate a fuzzy output, which reflects the uncertainties associated with its predictions. Part two gives a brief introduction to fuzzy decision-making and shows how it can be used to design a predictive control scheme that is suitable for controlling information-poor systems using inaccurate measurements. Part three describes different ways in which fuzzy relational models can be generated online and explains the practical issues associated with their identification and application. The final part of the book provides examples of the use of the previously described techniques in real applications. Key features: Describes techniques applicable to a wide range of engineering, environmental, medical, financial and economic applications Uses simple examples to help explain the basic techniques for dealing with uncertainty Describes a novel design approach based on the use of fuzzy relational models Considers practical issues associated with applying the techniques to real systems Monitoring and Control of Information-Poor Systems forms an invaluable resource for a wide range of graduate students, and is also a comprehensive reference for researchers and practitioners working on problems involving mathematical modelling and control.
Singh Pushpinder Ranking Approach to Solve Linear Programming Problems with Fuzzy Sets Singh Pushpinder Ranking Approach to Solve Linear Programming Problems with Fuzzy Sets Новинка

Singh Pushpinder Ranking Approach to Solve Linear Programming Problems with Fuzzy Sets

8927 руб.
Linear programming is one of the most frequently applied operations research techniques. The classical tool for solving the linear programming problem in practice is the class of simplex algorithm which was proposed and developed by Dantzig. A lot of real world decision problems are described by linear programming models and sometimes it is necessary to formulate them with elements of imprecision or uncertainty. This imprecise nature has long been studied with the help of the probability theory. However, the probability theory might not provide the correct interpretation to solve some practical decision making problems. In these cases, the fuzzy set theory might be more helpful. In this book, the limitations and shortcomings of existing methods for solving linear programming problems with fuzzy sets are pointed out. Some new ranking approaches for the ordering of fuzzy sets and vague sets are developed and also new methods to find the unique optimal solutions of linear programming problems under fuzzy environment and vague environment are presented.
Jerry Mendel Introduction To Type-2 Fuzzy Logic Control. Theory and Applications Jerry Mendel Introduction To Type-2 Fuzzy Logic Control. Theory and Applications Новинка

Jerry Mendel Introduction To Type-2 Fuzzy Logic Control. Theory and Applications

9993.65 руб.
An introductory book that provides theoretical, practical, and application coverage of the emerging field of type-2 fuzzy logic control Until recently, little was known about type-2 fuzzy controllers due to the lack of basic calculation methods available for type-2 fuzzy sets and logic—and many different aspects of type-2 fuzzy control still needed to be investigated in order to advance this new and powerful technology. This self-contained reference covers everything readers need to know about the growing field. Written with an educational focus in mind, Introduction to Type-2 Fuzzy Logic Control: Theory and Applications uses a coherent structure and uniform mathematical notations to link chapters that are closely related, reflecting the book’s central themes: analysis and design of type-2 fuzzy control systems. The book includes worked examples, experiment and simulation results, and comprehensive reference materials. The book also offers downloadable computer programs from an associated website. Presented by world-class leaders in type-2 fuzzy logic control, Introduction to Type-2 Fuzzy Logic Control: Is useful for any technical person interested in learning type-2 fuzzy control theory and its applications Offers experiment and simulation results via downloadable computer programs Features type-2 fuzzy logic background chapters to make the book self-contained Provides an extensive literature survey on both fuzzy logic and related type-2 fuzzy control Introduction to Type-2 Fuzzy Logic Control is an easy-to-read reference book suitable for engineers, researchers, and graduate students who want to gain deep insight into type-2 fuzzy logic control.
Murat Kulahci Introduction to Time Series Analysis and Forecasting Murat Kulahci Introduction to Time Series Analysis and Forecasting Новинка

Murat Kulahci Introduction to Time Series Analysis and Forecasting

10458.89 руб.
Praise for the First Edition «…[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics.» -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts. Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both popular and modern time series methodologies as well as an introduction to Bayesian methods in forecasting. Introduction to Time Series Analysis and Forecasting, Second Edition also includes: Over 300 exercises from diverse disciplines including health care, environmental studies, engineering, and finance More than 50 programming algorithms using JMP®, SAS®, and R that illustrate the theory and practicality of forecasting techniques in the context of time-oriented data New material on frequency domain and spatial temporal data analysis Expanded coverage of the variogram and spectrum with applications as well as transfer and intervention model functions A supplementary website featuring PowerPoint® slides, data sets, and select solutions to the problems Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook upper-undergraduate and graduate-levels courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.
Gerret Halberstadt On the informational content of asset prices for output (and inflation) forecasting Gerret Halberstadt On the informational content of asset prices for output (and inflation) forecasting Новинка

Gerret Halberstadt On the informational content of asset prices for output (and inflation) forecasting

1952 руб.
Seminar paper from the year 2014 in the subject Economics - Finance, grade: 1.0, Christian-Albrechts-University of Kiel, language: English, abstract: That financial markets can influence real economic activity has been accepted by economists long ago and became dramatically apparent again in the last financial crisis, when the sharp decline in housing prices in the US was followed by a severe recession. In general, asset prices are determined in a forward-looking manner, stock prices for example reflect the expected profitability of firms in the future and thus are linked to expected future economic conditions. Furthermore, many macroeconomic models suggested by economic theory in-corporate interest rates, interest spreads or exchange rates, which can be seen as some sort of financial assets, and believing in these models means believing that asset prices influence developments of macroeconomic v ariables in the future. These considerations and observations gave rise to examine the pre-dictive power of asset prices to forecast output and inflation and a survey of this literature as well as empirical tests for a variety of predictors in different countries can be found e.g. in Stock and Watson (2003).
P. Mouton R. Neurostereology. Unbiased Stereology of Neural Systems P. Mouton R. Neurostereology. Unbiased Stereology of Neural Systems Новинка

P. Mouton R. Neurostereology. Unbiased Stereology of Neural Systems

10969.18 руб.
Stereological methods provide researchers with unparalleled quantitative data from tissue samples and allow for well-evidenced research advances in a broad range of scientific fields. Presenting a concise introduction to the methodology and application of stereological research in neuroscience, Neurostereology provides a fuller understanding of the use of these methods in research and a means for replicating successful scientific approaches. Providing sound footing for future research, Neurostereology is a useful tool for basic and clinical researchers and advanced students looking to integrate these methods into their research.
P. Mouton R. Neurostereology. Unbiased Stereology of Neural Systems P. Mouton R. Neurostereology. Unbiased Stereology of Neural Systems Новинка

P. Mouton R. Neurostereology. Unbiased Stereology of Neural Systems

10686.74 руб.
Stereological methods provide researchers with unparalleled quantitative data from tissue samples and allow for well-evidenced research advances in a broad range of scientific fields. Presenting a concise introduction to the methodology and application of stereological research in neuroscience, Neurostereology provides a fuller understanding of the use of these methods in research and a means for replicating successful scientific approaches. Providing sound footing for future research, Neurostereology is a useful tool for basic and clinical researchers and advanced students looking to integrate these methods into their research.
Matthias Dehmer Computational Network Analysis with R. Applications in Biology, Medicine and Chemistry Matthias Dehmer Computational Network Analysis with R. Applications in Biology, Medicine and Chemistry Новинка

Matthias Dehmer Computational Network Analysis with R. Applications in Biology, Medicine and Chemistry

15880.91 руб.
This new title in the well-established «Quantitative Network Biology» series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.
Ali Moukadem Time-Frequency Domain for Segmentation and Classification of Non-stationary Signals. The Stockwell Transform Applied on Bio-signals and Electric Signals Ali Moukadem Time-Frequency Domain for Segmentation and Classification of Non-stationary Signals. The Stockwell Transform Applied on Bio-signals and Electric Signals Новинка

Ali Moukadem Time-Frequency Domain for Segmentation and Classification of Non-stationary Signals. The Stockwell Transform Applied on Bio-signals and Electric Signals

5887.26 руб.
This book focuses on signal processing algorithms based on the timefrequency domain. Original methods and algorithms are presented which are able to extract information from non-stationary signals such as heart sounds and power electric signals. The methods proposed focus on the time-frequency domain, and most notably the Stockwell Transform for the feature extraction process and to identify signatures. For the classification method, the Adaline Neural Network is used and compared with other common classifiers. Theory enhancement, original applications and concrete implementation on FPGA for real-time processing are also covered in this book.
Hans-Georg Nollau, Carmen Zech Forecasting. A Challenge for True Statisticians Hans-Georg Nollau, Carmen Zech Forecasting. A Challenge for True Statisticians Новинка

Hans-Georg Nollau, Carmen Zech Forecasting. A Challenge for True Statisticians

7064 руб.
At all times, looking into the future and knowing what is happening has been a dream of mankind. As a symbol for this attempt the Oracle of Delphi is the best proof and until today the Delphi-Method is an important decision support tool.Despite of all methods and procedures to make forecasting a high level of responsibility, seriousness and professionalism of all the involved people is an absolute necessity. Today, unfortunately we often have the situation where those who are putting society at risk are "no true statisticians", merely people using statistics either without understanding them or in a self-serving manner. This is not a joke, this is criminal!In the present contribution, the 16th volume of the publication series "Economy and Labour" with the title "Forecasting: A Challenge for True Statisticians", a scientific, well proofed method of mathematical statistics for Time Series Analysis and Forecasting is presented. It is one of the mathematically oriented methods and procedures of Customer-oriented-Holistic-Netted-Logistics CHNL described in this publication series.In the present volume an important forecasting tool is described and its power is impressively shown by case studies using the SCA-Software system.
Ahmed Moustafa A. Computational Models of Brain and Behavior Ahmed Moustafa A. Computational Models of Brain and Behavior Новинка

Ahmed Moustafa A. Computational Models of Brain and Behavior

11620.18 руб.
A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.
Mohammad Uruj Jaleel On Designing Energy Conserving WSN Mohammad Uruj Jaleel On Designing Energy Conserving WSN Новинка

Mohammad Uruj Jaleel On Designing Energy Conserving WSN

5787 руб.
Sensors are low cost tiny devices with limited storage, computational capability and power. They can be deployed in large scale for performing both military and civilian tasks. The main concern in Wireless Sensor Networks is how to utilize the limited energy resources. The performance of Wireless Sensor Networks strongly depends on their lifetime. As a result, Dynamic Power Management approaches with the purpose of reduction of energy consumption in sensor nodes, after deployment and designing of the network, have drawn attentions of many research studies. The neural-networks algorithms can lead to lower communication costs and energy conservation. All these characteristics show great analogy and compatibility between wireless sensor networks and neural networks. This book main focus is to present the most important possible application of neural networks in reduction of energy consumption.
A. Mohan Babu,M. Venkataramanaiah and M. Jahnavi Forecasting with Econometric Methods A. Mohan Babu,M. Venkataramanaiah and M. Jahnavi Forecasting with Econometric Methods Новинка

A. Mohan Babu,M. Venkataramanaiah and M. Jahnavi Forecasting with Econometric Methods

5465 руб.
Forecasting is an important aid in effective and efficient planning. It is an attempt to predict the feature by examining the past. The main purpose of making a forecast is to gain knowledge about uncertain events that are important to our present decisions. In the present study, an attempt has been made to forecast the production of prime food grains of Indian Agriculture sector like Rice, Wheat, Pulses and Oil seeds by using different types of ARIMA models. And also to fit the best suited time series model for the data collected with the help of forecasting accuracy measures.
Handbook of Mathematical Fuzzy Logic, Volume 3 Handbook of Mathematical Fuzzy Logic, Volume 3 Новинка

Handbook of Mathematical Fuzzy Logic, Volume 3

2539 руб.
Originating as an attempt to provide solid logical foundations for fuzzy set theory, and motivated also by philosophical and computational problems of vagueness and imprecision, Mathematical Fuzzy Logic (MFL) has become a significant subfield of mathematical logic. Research in this area focuses on many-valued logics with linearly ordered truth values and has yielded elegant and deep mathematical theories and challenging problems, thus continuing to attract an ever increasing number of researchers.This handbook provides, through its several volumes, an up-to-date systematic presentation of the best-developed areas of MFL. Its intended audience is researchers working on MFL or related fields, that may use the text as a reference book, and anyone looking for a comprehensive introduction to MFL. This handbook will be useful not only for readers interested in pure mathematical logic, but also for those interested in logical foundations of fuzzy set theory or in a mathematical apparatus suitable for dealing with some philosophical and linguistic issues related to vagueness.This third volume starts with three chapters on semantics of fuzzy logics, namely, on the structure of linearly ordered algebras, on semantic games, and on Ulam-Rényi games; it continues with an introduction to fuzzy logics with evaluated syntax, a survey of fuzzy description logics, and a study of probability on MV-algebras; and it ends with a philosophical chapter on the role of fuzzy logics in theories of vagu...
Zacharias Voulgaris, Yunus Emrah Bulut AI for Data Science. Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond Zacharias Voulgaris, Yunus Emrah Bulut AI for Data Science. Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond Новинка

Zacharias Voulgaris, Yunus Emrah Bulut AI for Data Science. Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond

4789 руб.
Master the approaches and principles of Artificial Intelligence (AI) algorithms, and apply them to Data Science projects with Python and Julia code.Aspiring and practicing Data Science and AI professionals, along with Python and Julia programmers, will practice numerous AI algorithms and develop a more holistic understanding of the field of AI, and will learn when to use each framework to tackle projects in our increasingly complex world.The first two chapters introduce the field, with Chapter 1 surveying Deep Learning models and Chapter 2 providing an overview of algorithms beyond Deep Learning, including Optimization, Fuzzy Logic, and Artificial Creativity.The next chapters focus on AI frameworks; they contain data and Python and Julia code in a provided Docker, so you can practice. Chapter 3 covers Apache’s MXNet, Chapter 4 covers TensorFlow, and Chapter 5 investigates Keras. After covering these Deep Learning frameworks, we explore a series of optimization frameworks, with Chapter 6 covering Particle Swarm Optimization (PSO), Chapter 7 on Genetic Algorithms (GAs), and Chapter 8 discussing Simulated Annealing (SA).Chapter 9 begins our exploration of advanced AI methods, by covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Chapter 10 discusses optimization ensembles and how they can add value to the Data Science pipeline.Chapter 11 contains several alternative AI frameworks including Extreme Learning Machines (ELMs), Capsule Networks (CapsN...
Suriani Ibrahim and Mohd Rafie Johan Conductivity Studies And Neural Networks Model Suriani Ibrahim and Mohd Rafie Johan Conductivity Studies And Neural Networks Model Новинка

Suriani Ibrahim and Mohd Rafie Johan Conductivity Studies And Neural Networks Model

2890 руб.
In the experiment, polyethylene oxide (PEO), lithium hexafluorophosphate (LiPF6), ethylene carbonate (EC) and carbon nanotube (CNT) were mixed ad various ratios. . The conductivity increases from 10-10 to 10-5 Scm-1 upon the addition of salt. The incorporation of EC and αCNTs to the salted polymer enhances the conductivity significantly to 10-4 and 10-3 Scm-1. In neural network training, different chemical composition and real impedance were used as inputs and imaginary impedance in the produced polymer electrolytes was used as outputs. After training process, the test data were used to check system accuracy. As a result, the neural network was found successful for the prediction of imaginary impedance of nanocomposite polymer electrolyte system.
David Price J. Building Brains. An Introduction to Neural Development David Price J. Building Brains. An Introduction to Neural Development Новинка

David Price J. Building Brains. An Introduction to Neural Development

5810.82 руб.
Provides a highly visual, readily accessible introduction to the main events that occur during neural development and their mechanisms Building Brains: An Introduction to Neural Development, 2nd Edition describes how brains construct themselves, from simple beginnings in the early embryo to become the most complex living structures on the planet. It explains how cells first become neural, how their proliferation is controlled, what regulates the types of neural cells they become, how neurons connect to each other, how these connections are later refined under the influence of neural activity, and why some neurons normally die. This student-friendly guide stresses and justifies the generally-held belief that a greater knowledge of how nervous systems construct themselves will help us find new ways of treating diseases of the nervous system that are thought to originate from faulty development, such as autism spectrum disorders, epilepsy, and schizophrenia. A concise, illustrated guide focusing on core elements and emphasizing common principles of developmental mechanisms, supplemented by suggestions for further reading Text boxes provide detail on major advances, issues of particular uncertainty or controversy, and examples of human diseases that result from abnormal development Introduces the methods for studying neural development, allowing the reader to understand the main evidence underlying research advances Offers a balanced mammalian/non-mammalian perspective (and emphasizes mechanisms that are conserved across species), drawing on examples from model organisms like the fruit fly, nematode worm, frog, zebrafish, chick, mouse and human Associated Website includes all the figures from the textbook and explanatory movies Filled with full-colorartwork that reinforces important concepts; an extensive glossary and definitions that help readers from different backgrounds; and chapter summaries that stress important points and aid revision, Building Brains: An Introduction to Neural Development, 2nd Edition is perfect for undergraduate students and postgraduates who may not have a background in neuroscience and/or molecular genetics. “This elegant book ranges with ease and authority over the vast field of developmental neuroscience. This excellent textbook should be on the shelf of every neuroscientist, as well as on the reading list of every neuroscience student.” —Sir Colin Blakemore, Oxford University “With an extensive use of clear and colorful illustrations, this book makes accessible to undergraduates the beauty and complexity of neural development. The book fills a void in undergraduate neuroscience curricula.” —Professor Mark Bear, Picower Institute, MIT. Highly Commended, British Medical Association Medical Book Awards 2012 Published with the New York Academy of Sciences
Karl Karsten Scientific Forecasting. Its Methods and Application to Practical Business and to Stock Market Operations Karl Karsten Scientific Forecasting. Its Methods and Application to Practical Business and to Stock Market Operations Новинка

Karl Karsten Scientific Forecasting. Its Methods and Application to Practical Business and to Stock Market Operations

1589 руб.
2015 Reprint of 1931 Edition. Full Facsimile of the original edition. Not reproduced with Optical Recognition Software. "Although the creation of the first hedge fund is usually credited to Alfred Winslow Jones, researches have recently discovered older indicators of hedge fund activity. The oldest source so far identified seems to be a book entitled "Scientific Forecasting" In it the author, Karl Karsten, summarized most the key principles of running a hedge fund." Hhabitant "Handbook of Hedge Funds."
Peter Hirschbichler Order Quantity Forecasting for the Fashion Industry Peter Hirschbichler Order Quantity Forecasting for the Fashion Industry Новинка

Peter Hirschbichler Order Quantity Forecasting for the Fashion Industry

3752 руб.
Master's Thesis from the year 2010 in the subject Computer Science - Applied, grade: 1, Fachhochschule Salzburg (Information Technology und Systems Management), language: English, abstract: Precise order quantity forecasting for fashion retailers is difficult, because of the specific nature of fashion products namely long lead times, seasonality, and product attributes suchas sizes, colours, and cuts. This thesis contributes to order quantity forecasting for fashion products by the use of regression analysis. For this purpose, forecasting techniques in general, and parametric as well as nonparametric regression analysis in articular are presented. This is followed by fundamentals of data mining, specifically data preprocessing and data warehousing, in order to be able to apply regression analysis on historical sales data. Furthermore, to examine the quality of forecasts a method forevaluating the economical benefit of order quantity forecasting was developed.As a next step, the presented methods for forecasting were applied to historical sales data. Therefore, sales data was analysed, regression models were applied and forecasts werecalculated and evaluated finally. This thesis is concluded by suggesting a forecasting implementation and by discussing the contributions to order quantity forecasting.
Asadullah Attal Develop of Neural Network Models to Predict Construction Cost/Duration Asadullah Attal Develop of Neural Network Models to Predict Construction Cost/Duration Новинка

Asadullah Attal Develop of Neural Network Models to Predict Construction Cost/Duration

5224 руб.
This Publication Contains the Analysis of Prediction of the Future highways Cost and Construction Duration based on the Artificial Neural Network Regression analysis. More than 1500 construction projects data was retrieved and more than 400 projects data was classified as the most important data for this analysis. The most effective factors were also identified by this regression analysis. As a result of this analysis, the outcome was more optimistic than our prediction.
Elena-Niculina Drăgoi,Silvia Curteanu and Vlad Dafinescu Modelling and optimization of chemical engineering processes Elena-Niculina Drăgoi,Silvia Curteanu and Vlad Dafinescu Modelling and optimization of chemical engineering processes Новинка

Elena-Niculina Drăgoi,Silvia Curteanu and Vlad Dafinescu Modelling and optimization of chemical engineering processes

7075 руб.
In the latest years, the use of computational techniques for solving different problems is rising. In this context, engineers are faced with the difficult choice of selecting and applying the best methods suited for modelling and optimization purposes. On the other hand, there are cases when the conventional approaches are not feasible and alternative methods must be developed. This book is aimed to help engineers in the application and implementation of neural networks and bio-inspired algorithms for solving day-to-day engineering problems and specific process optimization. New techniques based on artificial neural networks, differential evolution algorithm, and artificial immune systems are presented. They have proven to be flexible and efficient, having as main advantages the generalization capability and the opportunity of providing useful information for experimental practice.
H. Schwartz M. Multi-Agent Machine Learning. A Reinforcement Approach H. Schwartz M. Multi-Agent Machine Learning. A Reinforcement Approach Новинка

H. Schwartz M. Multi-Agent Machine Learning. A Reinforcement Approach

8831.63 руб.
The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces. Chapter 3 discusses two player games including two player matrix games with both pure and mixed strategies. Numerous algorithms and examples are presented. Chapter 4 covers learning in multi-player games, stochastic games, and Markov games, focusing on learning multi-player grid games—two player grid games, Q-learning, and Nash Q-learning. Chapter 5 discusses differential games, including multi player differential games, actor critique structure, adaptive fuzzy control and fuzzy interference systems, the evader pursuit game, and the defending a territory games. Chapter 6 discusses new ideas on learning within robotic swarms and the innovative idea of the evolution of personality traits. • Framework for understanding a variety of methods and approaches in multi-agent machine learning. • Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning • Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering
Saraswathi Muthusamy Fuzzy B-double star Opensets and fuzzy chi-double star Closed Sets Saraswathi Muthusamy Fuzzy B-double star Opensets and fuzzy chi-double star Closed Sets Новинка

Saraswathi Muthusamy Fuzzy B-double star Opensets and fuzzy chi-double star Closed Sets

3212 руб.
The concept of fuzzy set was introduced by Zadeh in his classical paper (Zadeh, 1965). Balasubramaniam and Sundaram defined generalized fuzzy closed sets in a fuzzy topological space and introduced certain types of fuzzy continuous functions between fuzzy topological spaces.(X, T) and (Y, S) denotes a fuzzy topological spaces.Also in this book I introduced different types of open sets and closed sets in fuzzy topological spaces.I think it will be very useful to co higher research in this field.
Vikram Santhanam Interference Mitigation Techniques for Heterogeneous Network Vikram Santhanam Interference Mitigation Techniques for Heterogeneous Network Новинка

Vikram Santhanam Interference Mitigation Techniques for Heterogeneous Network

3944 руб.
In this book, a new frequency reuse scheme is proposed to mitigate the inter-cell interference for the future OFDM based systems. In addition, a new frequency allocation technique and power allocation methods are illustrated. This provides an efficient spectrum allocation at the edge and center of the cell to co-ordinate with Femtocells and Macrocells for better network performance. It has been observed from the results that this novel technique with the Femtocells guarantees significantly increased data-rate, increased network power efficiency, frequency reuse factor of one through-out the cell, reduced inter-cell interference and increased network capacity with simple design to meet the QoS need of users. This frequency reuse scheme provides a solution for current challenges in wireless industries by offering a simple and efficient inter-cell interference coordination (ICIC) technique for the next-generation wireless network.
Kevin Warwick Introduction to Control Systems, an (2nd Edition) Kevin Warwick Introduction to Control Systems, an (2nd Edition) Новинка

Kevin Warwick Introduction to Control Systems, an (2nd Edition)

5339 руб.
This significantly revised edition presents a broad introduction to Control Systems and balances new, modern methods with the more classical. It is an excellent text for use as a first course in Control Systems by undergraduate students in all branches of engineering and applied mathematics. The book contains: A comprehensive coverage of automatic control, integrating digital and computer control techniques and their implementations, the practical issues and problems in Control System design; the three-term PID controller, the most widely used controller in industry today; numerous in-chapter worked examples and end-of-chapter exercises. This second edition also includes an introductory guide to some more recent developments, namely fuzzy logic control and neural networks.

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Provides a highly visual, readily accessible introduction to the main events that occur during neural development and their mechanisms Building Brains: An Introduction to Neural Development, 2nd Edition describes how brains construct themselves, from simple beginnings in the early embryo to become the most complex living structures on the planet. It explains how cells first become neural, how their proliferation is controlled, what regulates the types of neural cells they become, how neurons connect to each other, how these connections are later refined under the influence of neural activity, and why some neurons normally die. This student-friendly guide stresses and justifies the generally-held belief that a greater knowledge of how nervous systems construct themselves will help us find new ways of treating diseases of the nervous system that are thought to originate from faulty development, such as autism spectrum disorders, epilepsy, and schizophrenia. A concise, illustrated guide focusing on core elements and emphasizing common principles of developmental mechanisms, supplemented by suggestions for further reading Text boxes provide detail on major advances, issues of particular uncertainty or controversy, and examples of human diseases that result from abnormal development Introduces the methods for studying neural development, allowing the reader to understand the main evidence underlying research advances Offers a balanced mammalian/non-mammalian perspective (and emphasizes mechanisms that are conserved across species), drawing on examples from model organisms like the fruit fly, nematode worm, frog, zebrafish, chick, mouse and human Associated Website includes all the figures from the textbook and explanatory movies Filled with full-colorartwork that reinforces important concepts; an extensive glossary and definitions that help readers from different backgrounds; and chapter summaries that stress important points and aid revision, Building Brains: An Introduction to Neural Development, 2nd Edition is perfect for undergraduate students and postgraduates who may not have a background in neuroscience and/or molecular genetics. “This elegant book ranges with ease and authority over the vast field of developmental neuroscience. This excellent textbook should be on the shelf of every neuroscientist, as well as on the reading list of every neuroscience student.” —Sir Colin Blakemore, Oxford University “With an extensive use of clear and colorful illustrations, this book makes accessible to undergraduates the beauty and complexity of neural development. The book fills a void in undergraduate neuroscience curricula.” —Professor Mark Bear, Picower Institute, MIT. Highly Commended, British Medical Association Medical Book Awards 2012 Published with the New York Academy of Sciences
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