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Robust dissipativity and passivity analysis for discrete-time stochastic T-S fuzzy Cohen-Grossberg Markovian jump neural networks with mixed time delays. (English) Zbl 1349.93251

Summary: In this paper, we have concerned with the problem of dissipativity and passivity analysis for discrete-time stochastic Takagi-Sugeno (T-S) fuzzy Cohen-Grossberg neural networks with mixed time delays. The dynamical system is transformed into a T-S fuzzy model with uncertain parameters and Markovian jumping parameters. By employing the Lyapunov-Krasovskii functional method and linear matrix inequality (LMI) technique, some new sufficient conditions which are delay dependent in the sense that it depends on not only the discrete delay but also the infinitely distributed delay have been established to ensure the transformed fuzzy neural networks to be \(({\mathcal {Q}},{\mathcal {S}},{\mathcal {R}})-\gamma \)- dissipative and passive. Furthermore, the obtained dissipativity and passivity criteria are established in terms of LMIs, which can be easily checked by using the efficient MATLAB LMI toolbox. Finally, three numerical examples are provided to illustrate the effectiveness and less conservativeness of the obtained results.

MSC:

93C42 Fuzzy control/observation systems
68T05 Learning and adaptive systems in artificial intelligence
93D21 Adaptive or robust stabilization

Software:

LMI toolbox
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Full Text: DOI

References:

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