Adaptive synchronization of memristive neural networks with time-varying delays and reaction–diffusion term

Tu, Zhengwen; Ding, Nan; Li, Liangliang; Feng, Yuming; Zou, Limin; Zhang, Wei

doi:10.1016/j.amc.2017.05.005

Cited by 45 publications

(11 citation statements)

References 34 publications

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“…Citation information: DOI 10.1109/ACCESS.2020.3023715, IEEE Access Shaofang Wang et al: Finite-time synchronization of MMNNs with mixed time-varying delays find a similar form of controller (30) in Refs. [15], [34], [41], [55], etc. However, these research results either deal with asymptotic control, or deal with different networks models.…”

Section: B Non-linear Adaptive and Delay-independent State-feedback mentioning

confidence: 99%

“…Many scholars have devoted themselves to the study of dynamical behavior of MNNs, such as synchronization [5], stability and stabilization [6], [7], passivity [8]- [12], dispassvity [13], [14], etc. Up to now, many scholars have proposed many kinds of memristor-based neural networks models, such as reaction-diffusion memristor-based neural networks [5], [8], [12], [15], inertial memristor-based neural networks [9], [10], Hopfield memristor-based neural networks [7], VOLUME 4, 2016 Cohen-Grossberg memristor-based neural networks [16]- [18], fractional-order memristor-based neural networks [19]- [23], etc. Synchronization and stabilization are considered to be the most important dynamical behavior of MNNs.…”

Section: Introductionmentioning

confidence: 99%

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Finite-Time Synchronization of Multi-Linked Memristor-Based Neural Networks With Mixed Time-Varying Delays

Wang

Peng

et al. 2020

IEEE Access

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This paper mainly investigates the finite-time synchronization and stability issue of a class of master-slave multi-linked memristor-based neural networks (MMNNs) with mixed time-varying delays via different state-feedback controllers. Based on some synchronization analytical techniques and Lyapunov functional method, sufficient criteria are obtained to ensure that the master-slave MMNNs systems can realize finite-time synchronization under the Filippov-framework. Three different controllers are designed to synchronize the MMNNs systems, and the settling time of finite-time synchronization is estimated in advance. The correctness and the feasibility of the proposed synchronization criteria are confirmed by three simulation examples. INDEX TERMS Finite-time synchronization; Multi-linked memristor-based neural networks (MMNNs); Adaptive controller; Time-varying delays

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Section: B Non-linear Adaptive and Delay-independent State-feedback mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finite-Time Synchronization of Multi-Linked Memristor-Based Neural Networks With Mixed Time-Varying Delays

Wang

Peng

et al. 2020

IEEE Access

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“…Since Pecora and Carrol in [25] introduced the concept of drive-response synchronization for coupled chaotic systems, chaos synchronization has become a hot research topic due to its potential applications in secure communication, automatic control, biological systems, information science ( [26,27]). Also, the synchronization of neural networks has been the focus of scientific research and has been widely studied (see [28][29][30][31][32][33][34]).…”

Section: Introductionmentioning

confidence: 99%

Weighted pseudo-almost periodic solutions and global exponential synchronization for delayed QVCNNs

Lü

Meng

2019

J Inequal Appl

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In this paper, we study the existence of weighted pseudo-almost periodic solutions and the global exponential synchronization of delayed quaternion-valued cellular neural networks (QVCNNs). Firstly, we use the Banach fixed point theorem to establish the existence of weighted pseudo-almost periodic solutions for this class of QVCNNs. Then, under the condition that the drive system has a unique weighted pseudo-almost periodic solution, by designing a state-feedback controller and constructing suitable Lyapunov functions, we see that the drive-response structure of delayed QVCNNs with weighted pseudo-almost periodic coefficients achieve global exponential synchronization. Finally, a numerical example is given to illustrate the feasibility of our results.

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“…Time delays are ubiquitous in biology [13], physics [14], chemistry [15], optics [16], and complex networks [17,18]. Thus, time delays need to be considered in GRNs because of the finite speeds of the slow processes of transcription, translation, and translocation [7][8][9][10]12].…”

Section: Introductionmentioning

confidence: 99%

Desired clustering of genetic regulatory networks with mixed delays

et al. 2018

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In this study, we propose two techniques for clustering genetic regulatory networks with mixed delays. Laws for identifying coupling parameters are designed, and the Lyapunov theorem and Lipschitz condition are employed to achieve the desired stability and to identify the coupling parameters. Using our proposed methods, the value of a cluster or the sum of clusters can approach the expected values. Numerical examples verify the feasibility and effectiveness of this method.

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Adaptive synchronization of memristive neural networks with time-varying delays and reaction–diffusion term

Cited by 45 publications

References 34 publications

Finite-Time Synchronization of Multi-Linked Memristor-Based Neural Networks With Mixed Time-Varying Delays

Finite-Time Synchronization of Multi-Linked Memristor-Based Neural Networks With Mixed Time-Varying Delays

Weighted pseudo-almost periodic solutions and global exponential synchronization for delayed QVCNNs

Desired clustering of genetic regulatory networks with mixed delays

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