Nonlinear networks
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Nonlinear networks

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Published by Elsevier Scientific Pub. Co., distributors for the United States and Canada, Elsevier North-Holland in Amsterdam, New York, New York .
Written in English


  • Electric networks.,
  • Hilbert space.

Book details:

Edition Notes

Includes bibliographical references and index.

StatementVaclav Dolezal.
LC ClassificationsTK454.2 .D65 1977
The Physical Object
Paginationix, 156 p. :
Number of Pages156
ID Numbers
Open LibraryOL4535619M
ISBN 100444415718
LC Control Number77001464

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Based on the authors’ recent research, Nonlinear Control of Dynamic Networks provides a unified framework for robust, quantized, and distributed control under information constraints. Suggesting avenues for further exploration, the book encourages readers to take into consideration more communication and networking issues in control designs to better handle the arising challenges. Further, it discusses topics not typically found in standard textbooks, such as nonlinear operational amplifier circuits, nonlinear chaotic circuits and memristor networks. Each chapter includes a set of illustrative and worked examples, along with end-of-chapter exercises and lab exercises using the QUCS open-source circuit simulator. Delivering a systematic review of the nonlinear small-gain theorems, the text: Supplies novel cyclic-small-gain theorems for large-scale nonlinear dynamic networks Offers a cyclic-small-gain framework for nonlinear control with static or dynamic quantization Contains a combination of cyclic-small-gain and set-valued map designs for robust. This book addresses synchronization in networks of coupled systems. It illustrates the main aspects of the phenomenon through concise theoretical results and code, allowing readers to reproduce them and encouraging readers to pursue their own experimentation. The book begins by introducing the mathematical representation of nonlinear circuits and the code for their simulation.

Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models Dr. Oliver Nelles (auth.) The goal of this book is to provide engineers and scientIsts in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. This is simply the best book written on nonlinear control theory. The contents form the basis for feedback linearization techniques, nonlinear observers, sliding mode control, understanding relative degree, nonminimum phase systems, exact linearization, and a host of other topics. A careful reading of this book will provide vast rewards. - Buy Introduction to Nonlinear Circuits and Networks book online at best prices in India on Read Introduction to Nonlinear Circuits and Networks book reviews & author details and more at Free delivery on qualified : Bharathwaj Muthuswamy, Santo Banerjee. NONLINEAR NETWORK THEORY LEON 0. CHUA Associate Professor of Electrical Engineering Purdue Unl¥ersity McGRAW-HILL BOOK COMPANY New York St, Louis San Francisco London Sydney Toronto Mexico Panama. CONTENTS Preface, vii List of Reference Tables, xxv Glossary of Symbols and Abbreviations, xxvi.

  by Leon O. Chua. 9 Want to read. Published by R. E. Krieger Pub. Co. in Hungtington, N.Y. Written in English. Subjects. Nonlinear Electric networks, Nonlinear theories. There's no description for this book . Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, .   Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks. DOI link for Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks. Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks book. Fifteen years ago, nonlinear system identification was a field of several ad-hoc approaches, each applicable only to a very restricted class of systems. With the advent of neural networks, fuzzy models, and modern structure opti­ mization techniques a much wider class of systems can be handled.