Robustness analysis of hybrid stochastic neural networks with neutral terms and time-varying delays
type: Item
instance of: scholarly article
title: Robustness analysis of hybrid stochastic neural networks with neutral terms and time-varying delays
zbMATH Open document ID: 1418.92011
DOI: 10.1155/2015/278571
author: Junhao Hu
author: Yan Li
author: Chun-Mei Wu
published in: Discrete Dynamics in Nature and Society
publication date: 2019-02-19T00:00:00Z
review text: Summary: We analyze the robustness of global exponential stability of hybrid stochastic neural networks subject to neutral terms and time-varying delays simultaneously. Given globally exponentially stable hybrid stochastic neural networks, we characterize the upper bounds of contraction coefficients of neutral terms and time-varying delays by using the transcendental equation. Moreover, we prove theoretically that, for any globally exponentially stable hybrid stochastic neural networks, if additive neutral terms and time-varying delays are smaller than the upper bounds arrived, then the perturbed neural networks are guaranteed to also be globally exponentially stable. Finally, a numerical simulation example is given to illustrate the presented criteria.
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 93D09
zbMATH DE Number: 7025293
Wikidata QID: Q59105761
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/278571
OpenAlex ID: W1905857237
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description: scientific article; zbMATH DE number 7025293