Improved delay-dependent exponential stability criteria for discrete-time recurrent neural networks with time-varying delays

Authors:
Baoyong Zhang;Shengyuan Xu;Yun Zou
Affiliations:
School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, PR China;School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, PR China;School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, PR China
Venue:
Neurocomputing
Year:
2008

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Abstract

This paper is concerned with the problem of stability analysis for a class of discrete-time recurrent neural networks with time-varying delays. Under a weak assumption on the activation functions and using a new Lyapunov functional, a delay-dependent condition guaranteeing the global exponential stability of the concerned neural network is obtained in terms of a linear matrix inequality. It is shown that this stability condition is less conservative than some previous ones in the literature. When norm-bounded parameter uncertainties appear in a delayed discrete-time recurrent neural network, a delay-dependent robust exponential stability criterion is also presented. Numerical examples are provided to demonstrate the effectiveness of the proposed method.