Convergence Analysis of Recurrent Neural Networks

Overview

This volume provides a comprehensive study of the convergence of recurrent neural networks, which has been increasingly used in applications relating to associative memory, image processing and pattern recognition. Throughout the book, the authors present their original research results of recent years. While the main objective is to disseminate these results in a unified and comprehensive manner, the book is also written to be helpful to readers requiring basic information, such as methods and tools commonly ...

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Paperback (Softcover reprint of the original 1st ed. 2004)
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Overview

This volume provides a comprehensive study of the convergence of recurrent neural networks, which has been increasingly used in applications relating to associative memory, image processing and pattern recognition. Throughout the book, the authors present their original research results of recent years. While the main objective is to disseminate these results in a unified and comprehensive manner, the book is also written to be helpful to readers requiring basic information, such as methods and tools commonly used in the analysis and design of recurrent neural networks.
Audience: This volume is suitable for professionals and researchers, as well as advanced graduate level students in neural computations, and neural networks.

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Product Details

  • ISBN-13: 9781475738216
  • Publisher: Springer US
  • Publication date: 4/30/2014
  • Series: Network Theory and Applications Series, #13
  • Edition description: Softcover reprint of the original 1st ed. 2004
  • Pages: 233
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.54 (d)

Table of Contents

List of Figures
Preface
Acknowledgments
1 Introduction 1
2 Hopfield Recurrent Neural Networks 15
3 Cellular Neural Networks 33
4 Recurrent Neural Networks with Unsaturating Piecewise Linear Activation Functions 69
5 Lotka-Volterra Recurrent Neural Networks with Delays 91
6 Delayed Recurrent Neural Networks with Global Lipschitz Activation Functions 119
7 Other Models of Continuous Time Recurrent Neural Networks 171
8 Discrete Recurrent Neural Networks 195
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