Convergence Analysis of Recurrent Neural Networks Book

Convergence Analysis of Recurrent Neural Networks, 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, Convergence Analysis of Recurrent Neural Networks

3 out of 5 stars based on 2 reviews

Convergence Analysis of Recurrent Neural Networks
Written by author K. Tan
Published by Springer Netherlands, March 2004
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
Yi (School of Computer Science and Engineering, University of Electronic Science and Technology, China) and Tan (Department of Electrical and Computer Engineering, The National University of Singapore) present recent original research results on different

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Book Categories

Authors

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

Title: Convergence Analysis of Recurrent Neural Networks

Manufacturer:

Springer

Item Number: 9781402076947

Publication Date: March 2004

Number: 1

Product Description: Convergence Analysis of Recurrent Neural Networks

Universal Product Code (UPC): 9781402076947

WonderClub Stock Keeping Unit (WSKU): 9781402076947

Rating: 3/5 based on 2 Reviews

Image Location: https://wonderclub.com/images/covers/69/47/9781402076947.jpg

Category: Media >> Books

Weight: 0.200 kg (0.44 lbs)

Width: 6.420 cm (2.53 inches)

Heigh : 9.520 cm (3.75 inches)

Depth: 0.780 cm (0.31 inches)

Date Added: August 25, 2020, Added By: Ross

Date Last Edited: August 25, 2020, Edited By: Ross

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Alvis Champlin

reviewed Convergence Analysis of Recurrent Neural Networks on August 13, 2017

Convergence Analysis of Recurrent Neural Networks

Fascinating read but quite outdated

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Convergence Analysis of Recurrent Neural Networks, 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, Convergence Analysis of Recurrent Neural Networks

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Convergence Analysis of Recurrent Neural Networks Book

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	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