Robust asymptotic stability of uncertain fuzzy BAM neural networks with time-varying delays
Fuzzy Sets and Systems
Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach
IEEE Transactions on Fuzzy Systems
An analysis of global asymptotic stability of delayed cellular neural networks
IEEE Transactions on Neural Networks
Leakage Delays in T---S Fuzzy Cellular Neural Networks
Neural Processing Letters
Expert Systems with Applications: An International Journal
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Takagi-Sugeno (TS) fuzzy models are often used to describe complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear submodels. In this paper, the global robust stability problem of TS fuzzy cellular neural networks with parameter uncertainties and stochastic perturbations is investigated. Based on the Lyapunov method and stochastic analysis approaches, the globally robust asymptotically stable condition is presented in terms of linear matrix inequalities (LMIs), which can be easily solved by some standard numerical packages. A simulation example is provided to illustrate the effectiveness of the proposed criteria.