Non-Linear Feedback Neural Networks VLSI Implementations and Applica
This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and
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Mohd. Samar Ansari
Non-Linear Feedback Neural Networks VLSI Implementations and Applications
Studies in Computational Intelligence Volume 508
Series Editor J. Kacprzyk, Warsaw, Poland
For further volumes: http://www.springer.com/series/7092
Mohd. Samar Ansari
Non-Linear Feedback Neural Networks VLSI Implementations and Applications
123
Mohd. Samar Ansari Electronics Engineering Aligarh Muslim University Aligarh, UP India
ISSN 1860-949X ISBN 978-81-322-1562-2 DOI 10.1007/978-81-322-1563-9
ISSN 1860-9503 (electronic) ISBN 978-81-322-1563-9 (eBook)
Springer New Delhi Heidelberg New York Dordrecht London Library of Congress Control Number: 2013943949 Ó Springer India 2014 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
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Preface
Artificial Neural Networks (ANNs), having a highly parallel architecture, have emerged as a new paradigm for solving computationally intensive tasks using collective computation in a network of neurons. They can be considered as analog computers relying on simplified models of neurons. The essential difference between the ANN’s
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