Robust passivity analysis of fuzzy Cohen–Grossberg BAM neural networks with time-varying delays

Sakthivel, R.; Arunkumar, A.; Mathiyalagan, K.; Anthoni, S. Marshal

doi:10.1016/j.amc.2011.09.024

Cited by 70 publications

(40 citation statements)

References 29 publications

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“…Recently, in [17], mixed H ∞ and passivity-based state estimation for fuzzy neural networks with Markovian-type estimator gain change is studied. Moreover, the robust passivity analysis of fuzzy Cohen-Grossberg BAM neural networks with time-varying delays has been used on [18] by employing the delay fractioning technique with LMI optimization approach. Notably, the problem of dissipativity and passivity analysis for continuous and discrete time neural networks have already been widely studied, see e.g., [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Dissipativity and passivity analysis of T–S fuzzy neural networks with probabilistic time-varying delays: a quadratic convex combination approach

Nagamani

Radhika

2015

Nonlinear Dyn

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This paper studied dissipativity and passivity analysis of T-S fuzzy neural networks with distributed and probabilistic time-varying delay via quadratic convex combination approach. By introducing a stochastic variable with the Bernoulli distribution, the fuzzy neural networks with random time delays are transformed into one with deterministic delays and stochastic parameters. Moreover, it is well known that the dissipativity behavior of fuzzy neural networks is very sensitive to the time delay in the leakage term. By constructing proper Lyapunov-Krasovskii functional, new delay-dependent dissipativity and passivity conditions are derived in terms of linear matrix inequalities. Different from previous results, involving the first-order convex combination property, our derivation applies the idea of second-order convex combination and the property of quadratic convex function. Finally, numerical examples are provided to verify the effectiveness of the presented results.

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Section: Introductionmentioning

confidence: 99%

Dissipativity and passivity analysis of T–S fuzzy neural networks with probabilistic time-varying delays: a quadratic convex combination approach

Nagamani

Radhika

2015

Nonlinear Dyn

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“…The existence of time delays may lead to oscillation, divergence or instability of dynamical systems [2,3]. Various types of time-delay systems have been investigated, and many significant results have been reported [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

The stabilization of BAM neural networks with time-varying delays in the leakage terms via sampled-data control

Yang

Lin

2015

Neural Comput & Applic

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This paper is concerned with the stabilization of bidirectional associative memory neural networks with time-varying delays in the leakage terms using sampleddata control. We apply an input delay approach to change the sampling system into a continuous time-delay system. Based on the Lyapunov theory, some stability criteria are obtained. These conditions are expressed in terms of linear matrix inequalities and can be solved via standard numerical software. Finally, one numerical example is given to demonstrate the effectiveness of the proposed results.

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“…In the last three decades, considerable attention has been paid to BAM neural networks as well as various generalizations for their essential applications in classification, pattern recognition, optimization, signal and image processing, parallel computation, associative memory and nonlinear optimization problems and so on [1,2,7,9,11,13,15,17,21,25,28,29,30,33,34,37,38,39,40,41,42]. Recently, Li and Li [16] studied the following neutral delay BAM neural networks with time-varying delays in leakage terms…”

Section: Introductionmentioning

confidence: 99%

Existence and exponentially stability of anti-periodic solutions for neutral BAM neural networks with time-varying delays in the leakage terms

Xu¹,

Li²

2016

J. Nonlinear Sci. Appl.

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This paper is concerned with the existence and exponential stability of anti-periodic solutions of a neutral BAM neural network with time-varying delays in the leakage terms. Using some analysis skills and Lyapunov method, a series of sufficient conditions for the existence and exponential stability of anti-periodic solutions to the neutral BAM neural networks with time-varying delays in the leakage terms are presented. Our results are new and complement some previously known ones.

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Robust passivity analysis of fuzzy Cohen–Grossberg BAM neural networks with time-varying delays

Cited by 70 publications

References 29 publications

Dissipativity and passivity analysis of T–S fuzzy neural networks with probabilistic time-varying delays: a quadratic convex combination approach

Dissipativity and passivity analysis of T–S fuzzy neural networks with probabilistic time-varying delays: a quadratic convex combination approach

The stabilization of BAM neural networks with time-varying delays in the leakage terms via sampled-data control

Existence and exponentially stability of anti-periodic solutions for neutral BAM neural networks with time-varying delays in the leakage terms

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