| Summary: | 博士 === 國立成功大學 === 電機工程學系碩博士班 === 101 === A complete study of the control of time-delay Takagi-Sugeno fuzzy recurrent neural networks via the linear matrix inequality (LMI) approach and hybrid Taguchi-genetic algorithm (HTGA) is proposed in this dissertation. This includes the developments of the stabilization control design/passivity analysis/state estimator design for a class of time-delay Takagi-Sugeno (T-S) fuzzy uncertain recurrent neural networks, where the parameters uncertainties are assumed to be norm-bounded and time delays are interval time-varying delays. For the stabilization analysis, based on Lyapunov-Krasovskii functional approach and linear matrix inequality (LMI) technique, delay-dependent sufficient conditions are derived to guarantee the globally robustly exponential stability for the closed-loop time-delay T-S fuzzy recurrent neural networks. For the passivity analysis, the delay-dependent sufficient conditions are obtained by using Lyapunov-Krasovskii functional approach and linear matrix inequality (LMI) technique guarantees this stability of robust passivity for time-delay T-S fuzzy recurrent neural networks. Also, for state estimator design, we develop a new integrative technique to reduced remarkably and facilitate the design task of the estimator for computational complexity, a new hybrid Taguchi-genetic algorithm (HTGA) method is integrated with a linear matrix inequality (LMI) method to seek the estimator gains that satisfy the Lyapunov-Krasovskii functional stability inequalities. Finally, some illustrative examples are presented to demonstrate the effectiveness and applicability of our methodologies.
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