Chaos and asymptotical stability in discrete-time recurrent neural networks with generalized input-output function
type: Item
instance of: scholarly article
title: Chaos and asymptotical stability in discrete-time recurrent neural networks with generalized input-output function
zbMATH Open document ID: 1006.65139
DOI: 10.1007/BF02874421
published in: Science in China. Series A
publication date: 2002-08-15T00:00:00Z
review text: The conditions for asymptotic stability for the occurrance of bifurcations and finally for the appearance of chaotic behaviour beyond a threshold of \(\Delta t\) are analyzed and founded by means of a number of theorems in the case of discrete time recurrent neural networks with input-output functions in the form of a generalized sigmoid function. Limited numerical applications illustrate the theory.
Mathematics Subject Classification ID: 65P20
Mathematics Subject Classification ID: 65P30
Mathematics Subject Classification ID: 37D45
Mathematics Subject Classification ID: 65K10
Mathematics Subject Classification ID: 37M20
Mathematics Subject Classification ID: 93D25
Mathematics Subject Classification ID: 93D15
Mathematics Subject Classification ID: 93C55
Mathematics Subject Classification ID: 93D20
zbMATH DE Number: 1782078
zbMATH Keywords: chaos
zbMATH Keywords: asymptotic stability
zbMATH Keywords: bifurcation
zbMATH Keywords: neural network
zbMATH Keywords: snap-back repeller
zbMATH Keywords: numerical examples
zbMATH Keywords: discrete time
author: Jinliang Wang
author: Luonan Chen
author: Zhujun Jing
reviewed by: Maria Gousidou-Koutita
MaRDI profile type: Publication
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full work available at URL: https://doi.org/10.1007/bf02874421
OpenAlex ID: W3088209944
description: scientific article; zbMATH DE number 1782078