Delay-dependent globally exponential stability criteria for static neural networks: an LMI approach

Authors:
Cheng-De Zheng;Huaguang Zhang;Zhanshan Wang
Affiliations:
Department of Mathematics, Dalian Jiaotong University, Dalian, China;School of Information Science and Engineering, Northeastern University, Shenyang, China;School of Information Science and Engineering, Northeastern University, Shenyang, China
Venue:
IEEE Transactions on Circuits and Systems II: Express Briefs
Year:
2009

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Abstract

The problem of globally exponential stability of static neural networks is investigated. Based on the Lyapunov-Krasovskii functional approach, the free-weighting matrix method, and the Jensen integral inequality, new delay-dependent stability criteria of the unique equilibrium of static neural networks with time-varying delays are presented in terms of linear matrix inequalities (LMIs). The stability criteria can easily be checked by using recently developed algorithms in solving LMIs. A numerical example is given to illustrate the effectiveness and less conservativeness of our proposed method.