Stochastic exponential synchronization of memristive neural networks with time-varying delays via quantized control

Zhang, Wanli; Yang, Shiju; Zhang, Wei; Yang, Xinsong

doi:10.1016/j.neunet.2018.04.010

Cited by 50 publications

(17 citation statements)

References 40 publications

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“…Remark 1: Seen from the proposed network model, we select different activation functions for the items with selffeedback, discrete delay and distributed delay in MNNLs (1). When K = 1, the model (1) degenerates into the MNNs model [46]; When the memristors of…”

Section: A Description Of the Drive-response Systemsmentioning

confidence: 99%

Finite-Time Lag Synchronization of Memristive Neural Networks With Multi-Links via Adaptive Control

Wang¹,

Li²,

Peng³

et al. 2020

IEEE Access

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The finite-time lag synchronization problem of memristive neural networks with multi-links (MNNLs) is discussed with the adaptive controllers, and the discrete and distributed time-varying delays are included in the network models. With the Lyapunov functional method and the properties of inequality, two different finite-time methods are adopted to prove and compare the effects of the two proposed theorems, and three corollaries are obtained and to be analyzed. Using the assumptions of Lipschitz continuous and boundedness of the activation functions, three lemmas are derived to deal with the difficulty caused by the memristive jumping properties and synchronization lag feature, which make the proof of the theorems simple and clear. Based on that whether time-varying delays are included, with the adaptive control strategy, we design delay-independent and delay-dependent adaptive controllers with different adaptive parameters for the MNNLs respectively. Finally, two numerical simulations are given to show the effectiveness and correctness of the obtained theoretical results. INDEX TERMS Memristive neural networks with multi-links, finite-time lag synchronization, delaydependent adaptive control, delay-independent adaptive control, distributed time-varying delays.

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Section: A Description Of the Drive-response Systemsmentioning

confidence: 99%

Finite-Time Lag Synchronization of Memristive Neural Networks With Multi-Links via Adaptive Control

Wang¹,

Li²,

Peng³

et al. 2020

IEEE Access

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“…Assumption 3 is weaker than those investigated in other works. 2,[9][10][11][12]15,16,19,24,25 In reality, if we take R 4 ≡ 0, then Assumption 3 is the same as in the works of Zhu and Cao, 9 Sun et al, 19 and Dai et al 25 Assumption 4. (t, 0, 0, 0, 0) ≡ 0.…”

Section: Assumptionmentioning

confidence: 99%

“…In order to achieve this purpose, a large number of approaches have been proposed for the synchronization of chaotic systems in the literature, [1][2][3][4][5][6][7][8][9] such as adaptive control method, 1,2,9 impulsive control method, 3 feedback control method, 4 and sliding mode control method. 5 Recently, since synchronization of stochastic neural networks plays an important role in fundamental science and technological practice, it has attracted much attention of researchers and many results of research have been reported in the literature (see other works [9][10][11][12][13][14][15][16][17][18][19] and the references therein). Wang et al 10 and Zhang et al 11,12 considered the problem of exponential synchronization for stochastic neural networks with time-varying delays.…”

Section: Introductionmentioning

confidence: 99%

“…5 Recently, since synchronization of stochastic neural networks plays an important role in fundamental science and technological practice, it has attracted much attention of researchers and many results of research have been reported in the literature (see other works [9][10][11][12][13][14][15][16][17][18][19] and the references therein). Wang et al 10 and Zhang et al 11,12 considered the problem of exponential synchronization for stochastic neural networks with time-varying delays. It is worth noting that the exponential synchronization is the effective synchronization behavior because it can make two or more chaotic systems realize complete synchronization with the fastest rate.…”

Section: Introductionmentioning

confidence: 99%

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Almost surely asymptotic synchronization for stochastic neural networks of neutral type with Markovian jumping parameters

Xiong

Cao

et al. 2019

Adaptive Control & Signal

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This paper studies the problem of the almost surely asymptotic synchronization for a class of stochastic neural networks of neutral type with both Markovian jumping parameters and mixed time delays. Based on the stochastic analysis theory, LaSalle-type invariance principle, and delayed state-feedback control technique, some novel delay-dependent sufficient criteria to guarantee the almost surely asymptotic synchronization are given. These criteria are expressed as the linear matrix inequalities, which can be easily checked by MATLAB LMI Control Toolbox. Finally, four numerical examples and their simulations are provided to illustrate the effectiveness of the proposed method. KEYWORDS almost surely asymptotic synchronization, LaSalle invariant principle, Markovian jumping parameters, state-feedback control, stochastic neural network, time-varying delay 1524

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“…Stochastic differential equations (SDEs) have been intensively used to model the natural phenomena in last decades and these equations play a prominent applied role in various fields such as: Finance (Delong, 2013), Chemical (Coffey & Kalmykov, 2012), Biology (Wilkinson, 2011), Neural network (Zhang, et al, 2018), Prostate cancer (Assia & Wendi 2019) and so on. Most SDEs do not have explicit solutions.…”

Section: Introductionmentioning

confidence: 99%

A Note on Stability of Stochastic Logistic Model by Incorporating the Ornstein-Uhlenbeck Process

Ayoubi¹,

Khirzada²

2020

JMR

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In this research, we first prove that the stochastic logistic model (10) has a positive global solution. Subsequently, we introduce the sufficient conditions for the stochastically stability of the general form of stochastic differential equations (SDEs) in terms of equation (1), for zero solution by using the Lyapunov function. This result is verified via several examples in Appendix A. Besides; we prove that the stochastic logistic model, by incorporating the Ornstein-Uhlenbeck process is stable in zero solution. Furthermore, the simulated results are displayed via the 4-stage stochastic Runge-Kutta (SRK4) numerical method.

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Stochastic exponential synchronization of memristive neural networks with time-varying delays via quantized control

Cited by 50 publications

References 40 publications

Finite-Time Lag Synchronization of Memristive Neural Networks With Multi-Links via Adaptive Control

Finite-Time Lag Synchronization of Memristive Neural Networks With Multi-Links via Adaptive Control

Almost surely asymptotic synchronization for stochastic neural networks of neutral type with Markovian jumping parameters

A Note on Stability of Stochastic Logistic Model by Incorporating the Ornstein-Uhlenbeck Process

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