Exponential stability analysis for delayed neural networks with switching parameters: average dwell time approach

Authors:
Ligang Wu;Zhiguang Feng;Wei Xing Zheng
Affiliations:
Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, China;Department of Mechanical Engineering, The University of Hong Kong, Hong Kong;School of Computing and Mathematics, University of Western Sydney, New South Wales, Australia
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

This paper is concerned with the problem of exponential stability analysis of continuous-time switched delayed neural networks. By using the average dwell time approach together with the piecewise Lyapunov function technique and by combining a novel Lyapunov-Krasovskii functional, which benefits from the delay partitioning method, with the free-weighting matrix technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with constant and time-varying delays, respectively. Moreover, the decay estimates are explicitly given. The results reported in this paper not only depend upon the delay but also depend upon the partitioning, which aims at reducing the conservatism. Numerical examples are presented to demonstrate the usefulness of the derived theoretical results.