New delay-range-dependent stability condition for fuzzy Hopfield neural networks via Wirtinger inequality

Datta, Rupak; Dey, Rajeeb; Saravanakumar, Ramasamy; Bhattacharya, Baby; Lin, Tzu Chen

doi:10.3233/jifs-179694

Cited by 1 publication

(3 citation statements)

References 34 publications

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“…In 2015, Choi et al [29] investigated an L 2 − L ∞ filtering for the T-S fuzzy NNs in order to reduce the effect of external disturbances on the state estimation error of the T-S fuzzy NNs. Furthermore, Datta et al [21] investigated the asymptotic stability of the fuzzy HNNs with interval discrete time-varying delay. It is well known that the above model is a particular case of the T-S fuzzy GNNs, and distributed delay is unavoidable in the analysis of the delayed T-S fuzzy GNNs systems.…”

Section: B Extended Dissipative Analysismentioning

confidence: 99%

“…In addition, the neural networks model also has uncertainty or vagueness, so fuzzy logic has been applied to analyze the dynamical behavior of neural networks. For example, Datta et al [21] used T-S fuzzy logic to describe Hopfield neural networks (HNNs), and novel stability conditions for fuzzy HNNs are obtained by using Wirtinger inequality. The global exponential stability for the T-S fuzzy Cohen-Grossberg neural network is discussed in [22] by considering the effect of non-singular M-matrix properties and the Lyapunov stability technique.…”

Section: Introductionmentioning

confidence: 99%

“…The output consists of the disturbance, the state vector, and the state vector with interval discrete time-varying delay terms. So, our model is more general and covers others such as [6], [7], [15], [21], [26], [32], [35], [37], [38].…”

Section: Introductionmentioning

confidence: 99%

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Improved Extended Dissipativity Results for T-S Fuzzy Generalized Neural Networks With Mixed Interval Time-Varying Delays

Luemsai

Botmart

2022

IEEE Access

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The asymptotic stability and extended dissipativity performance of T-S fuzzy generalized neural networks (GNNs) with mixed interval time-varying delays are investigated in this paper. It is noted that this is the first time that extended dissipativity performance in the T-S fuzzy GNNs has been studied. To obtain the improved results, we construct the Lyapunov-Krasovskii functional (LKF), which consists of single, double, triple, and quadruple integral terms containing full information of the delays and a state variable. Moreover, an improved Wirtinger inequality, a new triple integral inequality, and zero equation, along with a convex combination approach, are used to deal with the derivative of the LKF. By using Matlab's LMI toolbox and the above methods, the new asymptotic stability and extended dissipativity conditions are gained in the form of linear matrix inequalities (LMIs), which include passivity, L 2 − L ∞ , H ∞ , and dissipativity performance. Finally, numerical examples that are less conservative than previous results are presented. Furthermore, we give numerical examples to demonstrate the correctness and efficacy of the proposed method for asymptotic stability and extended dissipativity performance of the T-S fuzzy GNNs, including a particular case of the T-S fuzzy GNNs. INDEX TERMS T-S fuzzy generalized neural networks, Asymptotic stability, Extended dissipativity analysis, Mixed interval time-varying delays.

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Section: B Extended Dissipative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%