LMI-based stability criterion for impulsive CGNNs via fixed point theory
type: Item
instance of: scholarly article
title: LMI-based stability criterion for impulsive CGNNs via fixed point theory
zbMATH Open document ID: 1394.34154
DOI: 10.1155/2015/281681
author: Shouming Zhong
author: Xiongrui Wang
author: Ruofeng Rao
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: Linear matrices inequalities (LMIs) method and the contraction mapping theorem were employed to prove the existence of globally exponentially stable trivial solution for impulsive Cohen-Grossberg neural networks (CGNNs). It is worth mentioning that it is the first time to use the contraction mapping theorem to prove the stability for CGNNs while only the Leray-Schauder fixed point theorem was applied in previous related literature. An example is given to illustrate the effectiveness of the proposed methods due to the large allowable variation range of impulse.
Mathematics Subject Classification ID: 34K20
Mathematics Subject Classification ID: 93D20
zbMATH DE Number: 6925945
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/281681
OpenAlex ID: W2173982193
Wikidata QID: Q59117842
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description: scientific article; zbMATH DE number 6925945