Exponential stability of uncertain stochastic neural networks with mixed time-delays

Zidong Wang, Stanislao Lauria, Fang, Jian’an and Xiaohui Liu

Chaos, Solitons & Fractals, 2007, vol. 32, issue 1, 62-72

Abstract: This paper is concerned with the global exponential stability analysis problem for a class of stochastic neural networks with mixed time-delays and parameter uncertainties. The mixed delays comprise discrete and distributed time-delays, the parameter uncertainties are norm-bounded, and the neural networks are subjected to stochastic disturbances described in terms of a Brownian motion. The purpose of the stability analysis problem is to derive easy-to-test criteria under which the delayed stochastic neural network is globally, robustly, exponentially stable in the mean square for all admissible parameter uncertainties. By resorting to the Lyapunov–Krasovskii stability theory and the stochastic analysis tools, sufficient stability conditions are established by using an efficient linear matrix inequality (LMI) approach. The proposed criteria can be checked readily by using recently developed numerical packages, where no tuning of parameters is required. An example is provided to demonstrate the usefulness of the proposed criteria.

Date: 2007
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:32:y:2007:i:1:p:62-72

DOI: 10.1016/j.chaos.2005.10.061

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