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

Content Provider	Springer Nature Link
Author	Ramasamy, S. Nagamani, G. Zhu, Quanxin
Copyright Year	2016
Abstract	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.
Starting Page	2777
Ending Page	2799
Page Count	23
File Format	PDF
ISSN	0924090X
Journal	Nonlinear Dynamics
Volume Number	85
Issue Number	4
e-ISSN	1573269X
Language	English
Publisher	Springer Netherlands
Publisher Date	2016-06-02
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dissipativity T–S fuzzy model Cohen–Grossberg neural networks Linear matrix inequality Lyapunov–Krasovskii functional Time-varying delay Vibration, Dynamical Systems, Control Mechanics Mechanical Engineering Automotive Engineering
Content Type	Text
Resource Type	Article
Subject	Ocean Engineering Applied Mathematics Control and Systems Engineering Mechanical Engineering Electrical and Electronic Engineering Aerospace Engineering

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