Delay-dependent robust stability for uncertain stochastic fuzzy Hopfield neural networks with time-varying delays

Authors:
Li Sheng;Ming Gao;Huizhong Yang
Affiliations:
School of Communication and Control Engineering, Jiangnan University, 1800 Lihu Rd., Wuxi, Jiangsu 214122, PR China and Institute for Systems Research, University of Maryland, College Park, MD 207 ...;College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266510, PR China;School of Communication and Control Engineering, Jiangnan University, 1800 Lihu Rd., Wuxi, Jiangsu 214122, PR China
Venue:
Fuzzy Sets and Systems
Year:
2009

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Abstract

Takagi-Sugeno (TS) fuzzy models are often used to represent complex nonlinear systems by means of fuzzy sets and fuzzy reasoning applied to a set of linear sub-models. In this paper, the global robust stability problem of TS fuzzy Hopfield neural networks with parameter uncertainties and stochastic perturbations is investigated. Based on the Lyapunov method and stochastic analysis approaches, the delay-dependent stability criterion is derived in terms of linear matrix inequalities (LMIs), which can be solved efficiently by using existing LMI optimization techniques. A simulation example is provided to illustrate the effectiveness of the developed method.