Stability analysis of discrete-time stochastic neural networks with time-varying delays

Authors:
Yan Ou;Hongyang Liu;Yulin Si;Zhiguang Feng
Affiliations:
Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, Heilongjiang 150001, PR China;Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, Heilongjiang 150001, PR China;Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, Heilongjiang 150001, PR China;Department of Mechanical Engineering, University of Hong Kong, Hong Kong
Venue:
Neurocomputing
Year:
2010

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Abstract

This paper investigates the problem of stability analysis for a class of discrete-time stochastic neural networks (DSNNs) with time-varying delays. In the concerned model, stochastic disturbances are described by a Brownian motion, and time-varying delay d(k) satisfies d"m@?d(k)@?d"M. Based on the delay partitioning idea and some inequalities, a new stability criterion with less conservatism in terms of linear matrix inequalities (LMIs) is proposed by introducing a novel Lyapunov-Krasovskii functional combined with a free-weighting matrix method. The condition can be checked by utilizing some numerical software and a numerical example is provided to show the usefulness of the proposed condition.