Associative memories based on discrete-time cellular neural networks with one-dimensional space-invariant templates

Zhigang Zeng, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, discrete-time cellular neural networks with one-dimensional space invariant are designed to associative memories. The obtained results enable both heteroassociative and autoassociative memories to be synthesized by assuring the global asymptotic stability of the equilibrium point and the feeding data via external inputs rather than initial conditions. It is shown that criteria herein can ensure the designed input matrix to be obtained by using one-dimensional space-invariant cloning template. Finally, one specific example is included to demonstrate the applicability of the methodology.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2006
Subtitle of host publicationThird International Symposium on Neural Networks, ISNN 2006, Proceedings
PublisherSpringer Verlag
Pages824-829
Number of pages6
ISBN (Print)354034439X, 9783540344391
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China
Duration: 28 May 20061 Jun 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3971 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Country/TerritoryChina
CityChengdu
Period28/05/061/06/06

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