Stability analysis of discrete-time recurrent neural networks with stochastic delay

Authors:
Yu Zhao;Huijun Gao;James Lam;Ke Chen
Affiliations:
Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, China and University of Southern California, Los Angeles, CA;Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, China;Department of Mechanical Engineering, University of Hong Kong, Hong Kong;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
Venue:
IEEE Transactions on Neural Networks
Year:
2009

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Abstract

This paper is concerned with the stability analysis of discrete-time recurrent neural networks (RNNs) with time delays as random variables drawn from some probability distribution. By introducing the variation probability of the time delay, a common delayed discrete-time RNN system is transformed into one with stochastic parameters. Improved conditions for the mean square stability of these systems are obtained by employing new Lyapunov functions and novel techniques are used to achieve delay dependence. The merit of the proposed conditions lies in its reduced conservatism, which is made possible by considering not only the range of the time delays, but also the variation probability distribution. A numerical example is provided to show the advantages of the proposed conditions.