Improved stochastic dissipativity of uncertain discrete-time neural networks with multiple delays and impulses

Raja, R.; Raja, U. Karthik; Samidurai, Rajendran; Leelamani, A.

doi:10.1007/s13042-013-0215-z

Cited by 13 publications

(2 citation statements)

References 38 publications

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“…In [25], the issue of L 2 -L ∞ state estimation design for delayed neural networks (NNs) is considered via quadratic-type generalized free-matrixbased integral inequality. The problem of dissipative analysis for aircraft flight control systems and uncertain discrete-time neural networks is addressed in [26,27]. It is well known that the concept of dissipativity was first studied by Willems [28].…”

Section: Introductionmentioning

confidence: 99%

Novel extended dissipativity criteria for generalized neural networks with interval discrete and distributed time-varying delays

Luemsai

Botmart

Weera³

2021

Adv Differ Equ

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The problem of asymptotic stability and extended dissipativity analysis for the generalized neural networks with interval discrete and distributed time-varying delays is investigated. Based on a suitable Lyapunov–Krasovskii functional (LKF), an improved Wirtinger single integral inequality, a novel triple integral inequality, and convex combination technique, the new asymptotic stability and extended dissipativity criteria are achieved for the generalized neural networks with interval discrete and distributed time-varying delays. By the above methods, the less conservative asymptotic stability criteria are obtained for a special case of the generalized neural networks. By using the Matlab LMI toolbox, the derived new asymptotic stability and extended dissipativity criteria are expressed in terms of linear matrix inequalities (LMIs) that cover $H_{\infty }$ H ∞ , $L_{2}$ L 2 –$L_{\infty }$ L ∞ , passivity, and dissipativity performance by setting parameters in the general performance index. Finally, we show numerical examples which are less conservative than other examples in the literature. Moreover, we present numerical examples for asymptotic stability and extended dissipativity performance of the generalized neural networks, including a special case of the generalized neural networks.

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Section: Introductionmentioning

confidence: 99%

Novel extended dissipativity criteria for generalized neural networks with interval discrete and distributed time-varying delays

Luemsai

Botmart

Weera³

2021

Adv Differ Equ

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“…Parametric uncertainty arises from a partial understanding of mathematical models, for instance, empirical quantities and constitutive laws (see [13][14][15][16][17][18][19][20][21][22]). e uncertainty of parameters must be considered in actual system because the parameters of the model in the process of industrial control are often uncertain.…”

Section: Introductionmentioning

confidence: 99%

Global Robust Exponential Synchronization of Multiple Uncertain Neural Networks Subject to Event‐Triggered Strategy

Zhang

Liu

2019

Complexity

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This paper proposes the event-triggered strategy (ETS) for multiple neural networks (NNs) with parameter uncertainty and time delay. By establishing event-triggered mechanism and using matrix inequality techniques, several sufficient criteria are obtained to ensure global robust exponential synchronization of coupling NNs. In particular, the coupling matrix need not be the Laplace matrix in this paper. In addition, the lower bounds of sampling time intervals are also found by the established event-triggered mechanism. Eventually, three numerical examples are offered to illustrate the obtained results.

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Improved stability analysis of uncertain neutral type neural networks with leakage delays and impulsive effects

Raja

Zhu

Senthilraj

et al. 2015

Applied Mathematics and Computation

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Improved stochastic dissipativity of uncertain discrete-time neural networks with multiple delays and impulses

Cited by 13 publications

References 38 publications

Novel extended dissipativity criteria for generalized neural networks with interval discrete and distributed time-varying delays

Novel extended dissipativity criteria for generalized neural networks with interval discrete and distributed time-varying delays

Global Robust Exponential Synchronization of Multiple Uncertain Neural Networks Subject to Event‐Triggered Strategy

Improved stability analysis of uncertain neutral type neural networks with leakage delays and impulsive effects

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