Robust stability for uncertain delayed fuzzy hopfield neural networks with Markovian jumping parameters

Authors:
Hongyi Li;Bing Chen;Qi Zhou;Weiyi Qian
Affiliations:
Institute of Complexity Science, Qingdao University, Qingdao, China;Institute of Complexity Science, Qingdao University, Qingdao, China;School of Automation, Nanjing University of Science and Technology, Nanjing, China;Department of Mathematics, Bohai University, Jinzhou, China
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Year:
2009

Citing 26
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Abstract

This paper is concerned with the problem of the robust stability of nonlinear delayed Hopfield neural networks (HNNs) with Markovian jumping parameters by Takagi-Sugeno (T-S) fuzzy model. The nonlinear delayed HNNs are first established as a modified T-S fuzzy model in which the consequent parts are composed of a set of Markovian jumping HNNs with interval delays. Time delays here are assumed to be time-varying and belong to the given intervals. Based on Lyapunov-Krasovskii stability theory and linear matrix inequality approach, stability conditions are proposed in terms of the upper and lower bounds of the delays. Finally, numerical examples are used to illustrate the effectiveness of the proposed method.