Stability Analysis of Cohen–Grossberg Type BAM Neural Network with Piecewise Constant Argument

This paper introduces the stability problems of Cohen–Grossberg type BAM neural network (BAMCGNN) with piecewise constant argument (PCA). By employing the homeomorphism theory, sufficient conditions for the existence and uniqueness of the equilibrium point are obtained; using inequality technique an...

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Main Authors: Wenqing Zheng, Tao Xie, Wenxiang Fang
Format: Article
Language:English
Published: Wiley 2023-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2023/2013300
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author Wenqing Zheng
Tao Xie
Wenxiang Fang
author_facet Wenqing Zheng
Tao Xie
Wenxiang Fang
author_sort Wenqing Zheng
collection DOAJ
description This paper introduces the stability problems of Cohen–Grossberg type BAM neural network (BAMCGNN) with piecewise constant argument (PCA). By employing the homeomorphism theory, sufficient conditions for the existence and uniqueness of the equilibrium point are obtained; using inequality technique and Lyapunov method, sufficient stability criteria for BAMCGNN with PCA are presented. Finally, a numerical case shows the significance of the results of this paper.
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institution Kabale University
issn 1607-887X
language English
publishDate 2023-01-01
publisher Wiley
record_format Article
series Discrete Dynamics in Nature and Society
spelling doaj-art-ab01f5ed4f1249d3937c0b9073f35caa2025-02-03T06:47:31ZengWileyDiscrete Dynamics in Nature and Society1607-887X2023-01-01202310.1155/2023/2013300Stability Analysis of Cohen–Grossberg Type BAM Neural Network with Piecewise Constant ArgumentWenqing Zheng0Tao Xie1Wenxiang Fang2School of Mathematics and StatisticsSchool of Mathematics and StatisticsSchool of Mathematics and StatisticsThis paper introduces the stability problems of Cohen–Grossberg type BAM neural network (BAMCGNN) with piecewise constant argument (PCA). By employing the homeomorphism theory, sufficient conditions for the existence and uniqueness of the equilibrium point are obtained; using inequality technique and Lyapunov method, sufficient stability criteria for BAMCGNN with PCA are presented. Finally, a numerical case shows the significance of the results of this paper.http://dx.doi.org/10.1155/2023/2013300
spellingShingle Wenqing Zheng
Tao Xie
Wenxiang Fang
Stability Analysis of Cohen–Grossberg Type BAM Neural Network with Piecewise Constant Argument
Discrete Dynamics in Nature and Society
title Stability Analysis of Cohen–Grossberg Type BAM Neural Network with Piecewise Constant Argument
title_full Stability Analysis of Cohen–Grossberg Type BAM Neural Network with Piecewise Constant Argument
title_fullStr Stability Analysis of Cohen–Grossberg Type BAM Neural Network with Piecewise Constant Argument
title_full_unstemmed Stability Analysis of Cohen–Grossberg Type BAM Neural Network with Piecewise Constant Argument
title_short Stability Analysis of Cohen–Grossberg Type BAM Neural Network with Piecewise Constant Argument
title_sort stability analysis of cohen grossberg type bam neural network with piecewise constant argument
url http://dx.doi.org/10.1155/2023/2013300
work_keys_str_mv AT wenqingzheng stabilityanalysisofcohengrossbergtypebamneuralnetworkwithpiecewiseconstantargument
AT taoxie stabilityanalysisofcohengrossbergtypebamneuralnetworkwithpiecewiseconstantargument
AT wenxiangfang stabilityanalysisofcohengrossbergtypebamneuralnetworkwithpiecewiseconstantargument