Projective synchronization of fuzzy memristive neural networks with pinning impulsive control

Fu, Qianhua; Zhong, Shouming; Jiang, Wenbo; Xie, Wenqian

doi:10.1016/j.jfranklin.2020.08.015

Cited by 40 publications

(21 citation statements)

References 38 publications

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“…Finally, illustrative examples are given to demonstrate the correctness and effectiveness of the proposed method. The proposed results and methods in this work are expected to be extended in the future topic to the exponential projective synchronization problem of T-S fuzzy GNNs, the extended dissipativity analysis of T-S fuzzy memristive GNNs, and so on [43], [45], [50].…”

Section: Discussionmentioning

confidence: 99%

“…Consider the T-S fuzzy GNNs (4) where i = 2 and Remark 7. In the future topic, it is very challenging to apply some lemmas or Lyapunov-Krasovskii functional used in this paper into the memristive systems [43]- [45] to achieve improved stability criteria, which are applied in next generation computer [46], [47] and powerful brain-like "neural" computer [48], [49].…”

Section: H(w Y(s))dsmentioning

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: Discussionmentioning

confidence: 99%

Section: H(w Y(s))dsmentioning

confidence: 99%

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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“…erefore, synchronization has become a hot spot studying the dynamic behavior of neural networks. So far, many types of synchronization for fuzzy neural networks have been proposed, for instance, exponential synchronization, antisynchronization, projective synchronization, and adaptive synchronization, for example, see [14][15][16][17][18][19]. However, in practical applications, we are not only interested in the synchronization performance of the system but also more concerned with the convergence time of the system.…”

Section: Introductionmentioning

confidence: 99%

Synchronization in Finite Time of Fuzzy Neural Networks with Hybrid Delays and Uncertain Nonlinear Perturbations

Zhao

2022

Advances in Fuzzy Systems

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This paper deals with the finite-time synchronization problem of a class of fuzzy neural networks with hybrid delays and uncertain nonlinear perturbations. By applying the famous finite-time stability theory, combining differential inequality techniques, and the analysis approach, several new algebraic sufficient criteria are obtained to realize finite-time synchronization between the drive system and the response system by designing a state feedback controller and an adaptive controller. Taking discrete delays, distributed delays, and uncertain nonlinear perturbations into account in fuzzy cellular neural networks makes the neural system more general than most existing cellular neural networks. Two different novel types of controllers designed to achieve finite-time synchronization can not only effectively overcome the influence of time delays and perturbations but also change their form according to the change of system state or perturbation to achieve a better control effect. Meanwhile, some algebraic sufficient criteria obtained in this paper can be proved by the parameters of the system itself, and the complex calculation of matrix inequality is avoided. Finally, the validity of our proposed results is confirmed by several examples and simulations. Furthermore, a secure communication problem is presented to further illustrate the fact of the obtained results.

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“…In addition it makes the modeling of practical nonlinear systems simple and better. The study of conic-type nonlinearities for dynamical system are addressed in [16]- [19]. An asynchronous filtering problem for T-S fuzzy Markovian jump system subject to nonhomogeneous transition probability is reported in [18].…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Asynchronous Fault Detection Filter Design for Conic-Type Nonlinear Semi-Markovian Jump Systems

Nithya¹,

Suveetha

Sakthivel³

et al. 2021

IEEE Access

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Projective synchronization of fuzzy memristive neural networks with pinning impulsive control

Cited by 40 publications

References 38 publications

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

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

Synchronization in Finite Time of Fuzzy Neural Networks with Hybrid Delays and Uncertain Nonlinear Perturbations

Finite-Time Asynchronous Fault Detection Filter Design for Conic-Type Nonlinear Semi-Markovian Jump Systems

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