Novel robust stability criteria of discrete-time stochastic recurrent neural networks with time delay

Authors:
Yijun Zhang;Shengyuan Xu;Zhenping Zeng
Affiliations:
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China;School of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China;School of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China
Venue:
Neurocomputing
Year:
2009

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Abstract

The problem of robust global exponential stability is investigated for a class of stochastic uncertain discrete-time recurrent neural networks with time delay. In this paper, the midpoint of the time delay's variation interval is introduced, and the variation interval is divided into two subintervals. Then, by constructing a new Lyapunov-Krasovskii functional and checking its variation in the two subintervals, respectively, some novel delay-dependent stability criteria for the addressed neural networks are derived. Numerical examples are provided to show that the achieved conditions are less conservative than some existing ones in the literature.