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A. Manivannan

Bio: A. Manivannan is an academic researcher from VIT University. The author has contributed to research in topics: Linear matrix inequality & Fuzzy logic. The author has an hindex of 6, co-authored 14 publications receiving 111 citations. Previous affiliations of A. Manivannan include Madurai Kamaraj University & Gandhigram Rural Institute.

Papers
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Journal ArticleDOI
TL;DR: In this article, robust stability analysis for Markovian jumping interval neural networks with discrete and distributed time-varying delays is investigated, and new delay-dependent stability criteria have been obtained in terms of linear matrix inequalities (LMIs).
Abstract: This paper investigates robust stability analysis for Markovian jumping interval neural networks with discrete and distributed time-varying delays. The parameter uncertainties are assumed to be bounded in given compact sets. The delay is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the new Lyapunov–Krasovskii functional (LKF), some inequality techniques and stochastic stability theory, new delay-dependent stability criteria have been obtained in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are given to illustrate the less conservative and effectiveness of our theoretical results.

29 citations

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TL;DR: A new global exponential stability condition is derived in terms of linear matrix inequality (LMI) by constructing new Lyapunov–Krasovskii functionals via generalized eigenvalue problems (GEVPs), formulated in the form of LMIs.

23 citations

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TL;DR: A novel sufficient condition is obtained in the form of linear matrix inequality such that the delayed stochastic neural networks are globally robustly asymptotically stable in the mean-square sense for all admissible uncertainties.
Abstract: The aim of this manuscript is to investigate the mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks with time-delays. The time-delays are assumed to be interval time-varying and randomly occurring. Based on the new Lyapunov–Krasovskii functional and stochastic analysis approach, a novel sufficient condition is obtained in the form of linear matrix inequality such that the delayed stochastic neural networks are globally robustly asymptotically stable in the mean-square sense for all admissible uncertainties. Finally, the derived theoretical results are validated through numerical examples in which maximum allowable upper bounds are calculated for different lower bounds of time-delay.

18 citations

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TL;DR: The main aim of the paper is to analyze the local and global stability of the class of mathematical models regarding the effect of time delays which provides a better pathway to the infection progress.

16 citations

Journal ArticleDOI
TL;DR: In this article, the robust stability for uncertain neutral stochastic system with Takagi-Sugeno (T-S) fuzzy model and Markovian jumping parameters (MJPs) is investigated.

16 citations


Cited by
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TL;DR: By employing an improved Lyapunov-Krasovskii functional and combining the reciprocal convex technique with the convex one, a new sufficient condition is derived to guarantee a class of delayed neural networks (DNNs) to be globally asymptotically stable.
Abstract: In this brief, by employing an improved Lyapunov-Krasovskii functional (LKF) and combining the reciprocal convex technique with the convex one, a new sufficient condition is derived to guarantee a class of delayed neural networks (DNNs) to be globally asymptotically stable. Since some previously ignored terms can be considered during the estimation of the derivative of LKF, a less conservative stability criterion is derived in the forms of linear matrix inequalities, whose solvability heavily depends on the information of addressed DNNs. Finally, we demonstrate by two numerical examples that our results reduce the conservatism more efficiently than some currently used methods.

96 citations

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TL;DR: Some improved delay-dependent stability criteria and dissipativity criteria are established in terms of linear matrix inequalities and the obtained criteria is extended to analyze the stability analysis of GNNs with two delay components and the passivity analysis of TSPs with one delay.
Abstract: This paper focuses on the problem of delay-dependent stability and dissipativity analysis of generalized neural networks (GNNs) with Markovian jump parameters and two delay components. By constructing novel augmented Lyapunov–Krasovskii functional (LKF), using free-matrix-based inequality to estimate the derivative of Lyapunov function and employing the reciprocally convex approach to consider the relationship between the time-varying delay and its interval, some improved delay-dependent stability criteria and dissipativity criteria are established in terms of linear matrix inequalities. Moreover, the obtained criteria is extended to analyze the stability analysis of GNNs with two delay components and the passivity analysis of GNNs with one delay. Numerical examples are given to show the effectiveness and the significant improvement of the proposed methods.

94 citations

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TL;DR: The problem of stability analysis of static recurrent neural networks with interval time-varying delay is investigated and some sufficient stability conditions are obtained in terms of linear matrix inequality (LMI).

79 citations

Journal ArticleDOI
TL;DR: In this paper, a class of uncertain neural networks with discrete interval and distributed time-varying delays and Markovian jumping parameters and some new delay-dependent criteria is derived to guarantee the mean-square asymptotic stability of the equilibrium point.

79 citations