Stochastic Stability of Delayed Neural Networks with Local Impulsive Effects

Wenbing Zhang; Yang Tang; Wai Keung Wong; Qingying Miao

doi:10.1109/TNNLS.2014.2380451

Stochastic Stability of Delayed Neural Networks with Local Impulsive Effects

Wenbing Zhang, Yang Tang, Wai Keung Wong, Qingying Miao

The Hong Kong Polytechnic University

Research output: Journal article publication › Journal article › Academic research › peer-review

49 Citations (Scopus)

Abstract

In this paper, the stability problem is studied for a class of stochastic neural networks (NNs) with local impulsive effects. The impulsive effects considered can be not only nonidentical in different dimensions of the system state but also various at distinct impulsive instants. Hence, the impulses here can encompass several typical impulses in NNs. The aim of this paper is to derive stability criteria such that stochastic NNs with local impulsive effects are exponentially stable in mean square. By means of the mathematical induction method, several easy-to-check conditions are obtained to ensure the mean square stability of NNs. Three examples are given to show the effectiveness of the proposed stability criterion.

Original language	English
Article number	06998862
Pages (from-to)	2336-2345
Number of pages	10
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	26
Issue number	10
DOIs	https://doi.org/10.1109/TNNLS.2014.2380451
Publication status	Published - 1 Oct 2015

Keywords

Impulsive systems
local impulsive effects
neural networks (NNs)
stability analysis

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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10.1109/TNNLS.2014.2380451

Cite this

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AB - In this paper, the stability problem is studied for a class of stochastic neural networks (NNs) with local impulsive effects. The impulsive effects considered can be not only nonidentical in different dimensions of the system state but also various at distinct impulsive instants. Hence, the impulses here can encompass several typical impulses in NNs. The aim of this paper is to derive stability criteria such that stochastic NNs with local impulsive effects are exponentially stable in mean square. By means of the mathematical induction method, several easy-to-check conditions are obtained to ensure the mean square stability of NNs. Three examples are given to show the effectiveness of the proposed stability criterion.

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Stochastic Stability of Delayed Neural Networks with Local Impulsive Effects

Abstract

Keywords

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