Analysis and design of associative memories for memristive neural networks with deviating argument
type: Item
instance of: scholarly article
title: Analysis and design of associative memories for memristive neural networks with deviating argument
zbMATH Open document ID: 1426.34107
DOI: 10.1155/2017/1057909
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: We investigate associative memories for memristive neural networks with deviating argument. Firstly, the existence and uniqueness of the solution for memristive neural networks with deviating argument are discussed. Next, some sufficient conditions for this class of neural networks to possess invariant manifolds are obtained. In addition, a global exponential stability criterion is presented. Then, analysis and design of autoassociative memories and heteroassociative memories for memristive neural networks with deviating argument are formulated, respectively. Finally, several numerical examples are given to demonstrate the effectiveness of the obtained results.
Mathematics Subject Classification ID: 34K20
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 34K19
Mathematics Subject Classification ID: 93D20
Mathematics Subject Classification ID: 94C30
zbMATH DE Number: 6971699
author: Jin E. Zhang
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/1057909
OpenAlex ID: W2743677550
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description: scientific article; zbMATH DE number 6971699