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Read online Neural Networks: A Comprehensive Foundation.pdf PDF, EPUB, MOBI, TXT, DOC Neural Networks: A Comprehensive Foundation Introducing students to the many facets of neural networks this text provides many case studies to illustrate their real life practical applications by Simon Haykin

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Neural Networks: A Comprehensive Foundation
Title:Neural Networks: A Comprehensive Foundation
Format Type:eBook PDF / e-Pub
Rating:
Author:
Published:
ISBN:0132733501
ISBN 13:
Number of Pages:842
Category:Computer science, Artificial intelligence, Textbooks, Neuroscience, Technology
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Adaptive Filter Theory, Least-Mean-Square Adaptive Filters, Digital Communications, Neural Networks And Learning Machines, Neural Networks: A Comprehensive Foundation, Communication Systems, An Introduction to Analog and Digital Communications, Kalman Filtering and Neural Networks, Signals and Systems, Modern Wireless Communications
This best selling easy to read communication systems text has been extensively revised to include the most exhaustive treatment of digital communications in an undergraduate level text In addition to being the most up to date communications text available Simon Haykin has added MATLAB computer experiments, b b This book provides a self motivating introduction to wireless communications it presents topics in a manner consistent with their natural evolution based on the principle of increasing spectral efficiency of the radio transmission b TOPICS b i Wireless Systems i begins with a discussion of FDMA systems and follows with the evolution through TDMA CDMA and SDMA techniques Engineering principles required for each multiple access strategy are presented parallel to it b b For electrical engineers and others involved in wireless communications, b State of the art coverage of Kalman filter methods for the design of neural networks b This self contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks Although the traditional approach to the subject is almost always linear this book recognizes and deals with the fact that real problems are most often nonlinear br br The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory Rauch Tung Striebel smoother and the extended Kalman filter Other chapters cover br br br An algorithm for the training of feedforward and recurrent multilayered perceptrons based on the decoupled extended Kalman filter DEKF Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics the expectation maximization EM algorithm and the extended Kalman smoothing EKS algorithm The unscented Kalman filter Each chapter with the exception of the introduction includes illustrative applications of the learning algorithms described here some of which involve the use of simulated and real life data Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems br br An Instructor s Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley Makerting Department, No description available