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.
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