A new criterion for exponential stability of uncertain stochastic neural networks with mixed delays

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Zhang, Jinhui, Shi, Peng, Qiu, Jiqing and Yang, Hongjiu (2008) A new criterion for exponential stability of uncertain stochastic neural networks with mixed delays. Mathematical and Computer Modelling, 47 (9-10). pp. 1042-1051. ISSN 0895-7177

Abstract

This paper deals with the problem of exponential stability for a class of uncertain stochastic neural networks with both discrete and distributed delays (also called mixed delays). The system possesses time-varying and norm-bounded uncertainties. Based on Lyapunov–Krasovskii functional and stochastic analysis approaches, new stability criteria are presented in terms of linear matrix inequalities to guarantee the delayed neural networks to be robustly exponentially stable in the mean square for all admissible parameter uncertainties. Numerical examples are given to illustrate the effectiveness of the developed techniques.

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Item type	Article
URI	https://vuir.vu.edu.au/id/eprint/4088
DOI	10.1016/j.mcm.2007.05.014
Official URL	http://dx.doi.org/10.1016/j.mcm.2007.05.014
Subjects	Historical > Faculty/School/Research Centre/Department > Institute for Logistics and Supply Chain Management (ILSCM) Historical > FOR Classification > 0199 Other Mathematical Sciences Information Systems
Keywords	ResPubID18757, stochastic neural networks, time delays, exponential stability, linear matrix inequalities (LMIs), norm-bounded uncertainties
Citations in Scopus	35 - View on Scopus
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