Robust dissipativity and passivity analysis for discrete-time stochastic T–S fuzzy Cohen–Grossberg Markovian jump neural networks with mixed time delays

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• 作者：S. Ramasamy ; G. Nagamani ; Quanxin Zhu
• 刊名：Nonlinear Dynamics
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：85
• 期：4
• 页码：2777-2799
• 全文大小：893 KB
• 刊物类别：Engineering
• 刊物主题：Vibration, Dynamical Systems and Control
  Mechanics
  Mechanical Engineering
  Automotive and Aerospace Engineering and Traffic
  
• 出版者：Springer Netherlands
• ISSN：1573-269X
• 卷排序：85

文摘

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.KeywordsDissipativityT–S fuzzy modelCohen–Grossberg neural networksLinear matrix inequalityLyapunov–Krasovskii functionalTime-varying delay