Letters: Mean square exponential stability of stochastic fuzzy Hopfield neural networks with discrete and distributed time-varying delays

Authors:
Hongyi Li;Bing Chen;Chong Lin;Qi Zhou
Affiliations:
Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 15001, PR China;Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 15001, PR China;Institute of Complexity Science, Qingdao University, Qingdao 266071, PR China;School of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China
Venue:
Neurocomputing
Year:
2009

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Abstract

It is well known that a complex nonlinear system can be represented as a Takagi-Sugeno (T-S) fuzzy model that consists of a set of linear sub-models. This paper is concerned with the problem of mean square exponential stability for a class of stochastic fuzzy Hopfield neural networks with discrete and distributed time-varying delays. By using the stochastic analysis approach and Ito@^ differential formula, delay-dependent conditions ensuring the stability of the considered neural networks are obtained. The conditions are expressed in terms of linear matrix inequalities (LMIs) and can be easily checked by standard software. A numerical example is given to illustrate the effectiveness of the proposed method.