Exponential stability of uncertain stochastic fuzzy BAM neural networks with time-varying delays

Among the various fuzzy models, the well-known Takagi-Sugeno (TS) fuzzy model is recognized as a popular and powerful tool in approximating a complex nonlinear system. TS model provides a fixed structure to some nonlinear systems and facilitates the analysis of the system. This paper concerns with the global exponential stability of uncertain stochastic bidirectional associative memory (BAM) neural networks with time-varying delays which are represented by the TS fuzzy models. The stability conditions are derived using Lyapunov-Krasovskii approach, in combination with the linear matrix inequality (LMI) techniques. Finally, numerical examples are given to demonstrate the correctness of the theoretical results.

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