Global exponential estimates of stochastic interval neural networks with discrete and distributed delays

Authors:
Zhan Shu;James Lam
Affiliations:
Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong;Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong
Venue:
Neurocomputing
Year:
2008

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Abstract

This paper is concerned with the robust exponential estimating problem for a class of neural networks with discrete and distributed delays. The considered neural networks are disturbed by Wiener processes, and possess interval uncertainties in the system parameters. A sufficient condition, which does not only guarantee the global exponential stability but also provides more exact characterizations on the decay rate and the coefficient, is established in terms of a novel Lyapunov-Krasovskii functional equipped with appropriately constructed exponential terms and the linear matrix inequality (LMI) technique. The estimates of the decay rate and the coefficient are obtained by solving a set of LMIs, which can be implemented easily by effective algorithms. In addition, slack matrices are introduced to reduce the conservatism of the condition. A numerical example is provided to illustrate the effectiveness of the theoretical results.