Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays

Wen, Shiping; Bao, Gang; Zeng, Zhigang; Chen, Yiran; Huang, Tingwen

doi:10.1016/j.neunet.2013.10.001

Cited by 173 publications

(66 citation statements)

References 40 publications

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“…In addition, it is known that Cohen-Grossberg neural network includes some well-known neural networks such as Hopfield neural networks, cellular neural networks and recurrent neural networks as a special case. From this point, we can conclude that our results are more practical than those in , Wu et al (2012), Wen et al (2013), Li and Cao (2015), , Zhang and Shen (2014).…”

Section: \0;mentioning

confidence: 52%

“…In Wu et al (2012), Wen et al (2013), Li and Cao (2015), Shen (2013, 2014), based on the theory of differential inclusions, the authors studied the complete synchronization of various types of memristor-based cellular neural networks. In this paper, for the special case aðtÞ ¼ À1, the anti-synchronization can be achieved, and for the special case aðtÞ ¼ 1, the complete synchronization can be achieved.…”

Section: \0;mentioning

confidence: 99%

“…Because of the unpredictability of the scaling function aðtÞ in this function projective synchronization can additionally enhance the security of communication than other complete synchronization or antisynchronization papers (Yang et al 2014;Wu et al 2012;Wen et al 2013;Li and Cao 2015;Shen 2013, 2014). From this point, our results are superior and have greater applicability.…”

Section: \0;mentioning

confidence: 99%

“…As a result, Cohen-Grossberg neural networks have attracted considerable research attention and many good results related have been reported on the boundedness, stability, convergence, synchronization and other oscillatory properties of Cohen-Grossberg neural networks (Cao and Liang 2004;Lisena 2011;Zhu and Cao 2010;. In addition, it has been reported that the memristorbased neural networks can exhibit some complicated dynamics and even chaotic behavior, and synchronization of chaotic neural networks based on memristor has also become an important area of study (Yang et al 2014;Wu et al 2012;Wen et al 2013;Li and Cao 2015;Shen 2013, 2014;Bao and Cao 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In , the antisynchronization of memristive recurrent neural networks with time-varying delays was studied by using differential inclusions theory and Lyapunov functional method. In Wu et al (2012), Wen et al (2013), Li and Cao (2015), , Zhang and Shen (2014), based on the theory of differential equations with discontinuous righthand side as introduced by Filippov, the authors investigated the complete synchronization of various types of memristor-based neural networks with or without delays. In , the finite-time synchronization of memristor-based neural networks was studied by using finite time stability theory and differential inclusions theory.…”

Section: Introductionmentioning

confidence: 99%

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Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays

2015

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This paper deals with the problem of function projective synchronization for a class of memristor-based Cohen-Grossberg neural networks with time-varying delays. Based on the theory of differential equations with discontinuous right-hand side, some novel criteria are obtained to realize the function projective synchronization of addressed networks by combining open loop control and linear feedback control. As some special cases, several control strategies are given to ensure the realization of complete synchronization, anti-synchronization and the stabilization of the considered memristor-based CohenGrossberg neural network. Finally, a numerical example and its simulations are provided to demonstrate the effectiveness of the obtained results.

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Section: \0;mentioning

confidence: 52%

Section: \0;mentioning

confidence: 99%

Section: \0;mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays

2015

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Exponential Synchronization of Memristive Chaotic Recurrent Neural Networks Via Alternate Output Feedback Control

Fang

2017

Asian Journal of Control

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This paper considers the global exponential synchronization problem of two memristive chaotic recurrent neural networks with time-varying delays using periodically alternate output feedback control. First, the periodically alternate output feedback control rule is designed for the global exponential synchronization of two memristive chaotic recurrent neural networks. Then, according to the Lyapunov stability theory, we construct an appropriate Lyapunov-Krasovskii functional to derive several new sufficient conditions guaranteeing exponential synchronization of two memristive chaotic recurrent neural networks under periodically alternate output feedback control. Compared with existing results on synchronization conditions on the basis of linear matrix inequalities of memristive chaotic recurrent neural networks, the derived results complement, extend earlier related results, and are also easy to validate in this paper. An illustrative example is provided to illustrate the effectiveness of the synchronization criteria.

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Stability analysis of memristor‐based complex‐valued recurrent neural networks with time delays

et al. 2014

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This article addresses stability analysis of a general class of memristor‐based complex‐valued recurrent neural networks (MCVNNs) with time delays. Some sufficient conditions to guarantee the boundedness on a compact set that globally attracts all trajectories of the MCVNNs are obtained by utilizing local inhibition. Moreover, some sufficient conditions for exponential stability and the global stability of the MCVNNs are established with the help of local invariant sets and linear matrix inequalities using Lyapunov–Krasovskii functional. The analysis results in the article, based on the results from the theory of differential equations with discontinuous right‐hand sides as introduced by Filippov. Finally, two numerical examples are also presented to show the effectiveness and usefulness of our theoretical results. © 2014 Wiley Periodicals, Inc. Complexity 21: 14–39, 2016

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Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays

Cited by 173 publications

References 40 publications

Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays

Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays

Exponential Synchronization of Memristive Chaotic Recurrent Neural Networks Via Alternate Output Feedback Control

Stability analysis of memristor‐based complex‐valued recurrent neural networks with time delays

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