Finite-time passivity for neutral-type neural networks with time-varying delays – via auxiliary function-based integral inequalities

Saravanan, S.; Ali, M. Syed; Alsaedi, Ahmed; Ahmad, Bashir

doi:10.15388/namc.2020.25.16513

Cited by 7 publications

(6 citation statements)

References 33 publications

(46 reference statements)

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“…Remark 4 In recent years, the WSII was developed in [40] to estimate the derivatives of LKFs with a single integral term. Several studies have used the improved WSII to estimate the derivative of LKFs, for example, in [42], the authors have obtained criteria of finite-time passivity for neutral-type neural networks with time-varying delays by using the improved WSII with Jensen's inequality. In 2018, a novel triple integral inequality was constructed in [39] to estimate the derivative of LKFs with the triple integral term, moreover, the authors achieved an improved delay-dependent exponential stability criterion by using a novel triple integral inequality with the extended reciprocally convex technique.…”

Section: Theorem 33 For Given Scalarsmentioning

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: Theorem 33 For Given Scalarsmentioning

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 authors considered the robust finite-time H ∞ in singular stochastic systems problem in [55]. Finite-time passivity in neural networks has been considered in [3]. However, unfortunately, the finite-time with H ∞ /passivity for generalized neural networks has not yet been studied.…”

Section: Introductionmentioning

confidence: 99%

Finite-Time MixedH_∞/Passivity for Neural Networks With Mixed Interval Time-Varying Delays Using the Multiple Integral Lyapunov-Krasovskii Functional

et al. 2021

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In this article, we consider the finite-time mixed H ∞ /passivity, finite-time stability, and finite-time boundedness for generalized neural networks with interval distributed and discrete time-varying delays. It is noted that this is the first time for studying in the combination of H ∞ , passivity, and finitetime boundedness. To obtain several sufficient criteria achieved in the form of linear matrix inequalities (LMIs), we introduce an appropriate Lyapunov-Krasovskii function (LKF) including single, double, triple, and quadruple integral terms, and estimating the bound of time derivative in LKF with the use of Jensen's integral inequality, an extended single and double Wirtinger's integral inequality, and a new triple integral inequality. These LMIs can be solved by using MATLAB's LMI toolbox. Finally, five numerical simulations are shown to illustrate the effectiveness of the obtained results. The received criteria and published literature are compared.INDEX TERMS neural networks, Lyapunov-Krasovskii function, H ∞ and passivity, time-varying delays, finite-time bounded

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“…The authors of [32] used the Lyapunov-Krasovskii functional approach and weighting matrices to study passivity analysis of stochastic time-delay neural networks. In addition, the subject of passivity analysis for various neural networks has received a lot of attention [33][34][35][36][37][38][39][40][41][42]. To the best of authors knowledge, so far, no result on the finite-time passivity for complex valued neural network systems with time varying delay has been reported.…”

Section: Introductionmentioning

confidence: 99%

Passivity analysis of multiple time-varying time delayed complex variable neural networks in finite-time

Jayanthi¹,

Santhakumari²

2021

Math. Model. Comput.

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In this article, we investigate the problem of finite-time passivity for the complex-valued neural networks (CVNNs) with multiple time-varying delays. To begin, many definitions relevant to the finite-time passivity of CVNNs are provided; then the suitable control inputs are designed to guarantee the class of CVNNs are finite-time passive. In the meantime, some sufficient conditions of linear matrix inequalities (LMIs) are derived by using inequalities techniques and Lyapunov stability theory. Finally, a numerical example is presented to illustrate the usefulness of the theoretical results.

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Finite-time passivity for neutral-type neural networks with time-varying delays – via auxiliary function-based integral inequalities

Cited by 7 publications

References 33 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

Finite-Time MixedH_∞/Passivity for Neural Networks With Mixed Interval Time-Varying Delays Using the Multiple Integral Lyapunov-Krasovskii Functional

Passivity analysis of multiple time-varying time delayed complex variable neural networks in finite-time

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Finite-time passivity for neutral-type neural networks with time-varying delays – via auxiliary function-based integral inequalities

Cited by 7 publications

References 33 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

Finite-Time MixedH∞/Passivity for Neural Networks With Mixed Interval Time-Varying Delays Using the Multiple Integral Lyapunov-Krasovskii Functional

Passivity analysis of multiple time-varying time delayed complex variable neural networks in finite-time

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Finite-Time MixedH_∞/Passivity for Neural Networks With Mixed Interval Time-Varying Delays Using the Multiple Integral Lyapunov-Krasovskii Functional