Robust synchronization of uncertain Markovian jump complex dynamical networks with time-varying delays and reaction–diffusion terms via sampled-data control

Ozcan, Neyir; Ali, M. Syed; Yogambigai, J.; Zhu, Quanxin; Arik, Sabri

doi:10.1016/j.jfranklin.2017.12.016

Cited by 43 publications

(7 citation statements)

References 45 publications

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“…Noting that ( 35) can be ensured by (41), one only needs to check the validity of (36). Using (20) and (22), it is not difficult to get that…”

Section: It Is Not Difficult To Re-express (30) Into the Followingmentioning

confidence: 99%

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Fault-tolerant anti-synchronization control for chaotic switched neural networks with time delay and reaction diffusion

Zhou¹,

Liu²,

Park³

et al. 2021

Discrete &Amp; Continuous Dynamical Systems - S

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This paper is concerned with the issue of fault-tolerant anti-synchronization control for chaotic switched neural networks with time delay and reaction-diffusion terms under the drive-response scheme, where the response system is assumed to be disturbed by stochastic noise. Both arbitrary switching signal and average dwell-time limited switching signal are taken into account. With the aid of the Lyapunov-Krasovskii functional approach and combining with the generalized Itô formula, sufficient conditions on the mean-square exponential stability for the anti-synchronization error system are presented. Then, by utilizing some decoupling methods, constructive design strategies on the desired fault-tolerant anti-synchronization controller are proposed. Finally, an example is given to demonstrate the effectiveness of our design strategies.

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“…Noting that ( 35) can be ensured by (41), one only needs to check the validity of (36). Using (20) and (22), it is not difficult to get that…”

Section: It Is Not Difficult To Re-express (30) Into the Followingmentioning

confidence: 99%

“…[30,28,38,19,25]. Note that the constraint on the activation functions in (10) is weaker than those imposed in [22,17,33,12,7].…”

mentioning

confidence: 99%

Fault-tolerant anti-synchronization control for chaotic switched neural networks with time delay and reaction diffusion

Zhou¹,

Liu²,

Park³

et al. 2021

Discrete &Amp; Continuous Dynamical Systems - S

View full text Add to dashboard Cite

show abstract

“…Since the applications of neural networks (NNs) heavily depend on their dynamic behavior the synchronization analysis of such systems is a necessary step to their practical design [1]- [3]. The reaction-diffusion systems have attracted much attention because of their wide applications in chemical processes, biological populations and NNs [4], [5]. Such kind of systems can be expressed by partial differential equations, in which the states are dependent on both time and space.…”

Section: Introductionmentioning

confidence: 99%

Impulsive Synchronization Control Mechanism for Fractional-Order Complex-Valued Reaction-Diffusion Systems With Sampled-Data Control: Its Application to Image Encryption

et al. 2022

Self Cite

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This article is devoted to gain a better understanding of the synchronization control mechanism in networked, complex-valued reaction-diffusion systems via an impulsive and sampled-data controller. Different from the traditional control methods with hybrid mechanism, an impulsive and sampled-data control scheme is proposed for fractional-order complex-valued reaction-diffusion neural networks (FRDCVNNs). By utilizing Lyapunov functional and inequality techniques, finite-time Mittag-Leffler synchronization criteria via an impulsive and sampled-data controller are established and presented as linear matrix inequalities (LMIs), which can ensure the finite-time synchronization of error system containing the drive and response dynamics. In addition, synchronization control mechanism on the considered system via impulsive actuator saturation and sampled-data control are applied. Simulation examples are provided to verify the efficacy of the proposed synchronization criterion and the results of practical application to image encryption scheme based on the chaotic complex system is presented. Furthermore, the proposed cryptosystem have obvious advantages of large key space and high security.

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“…This sets off a research upsurge for the dynamics of neural networks. As a result, many researchers have put their efforts into the analysis and synthesis problems for the dynamics of neural networks for several decades; see [1][2][3][4][5][6][7][8] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Finite‐Time Synchronization for Complex‐Valued Recurrent Neural Networks with Time Delays

Zhang

Lin

et al. 2018

Complexity

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This paper focuses on the finite-time synchronization analysis for complex-valued recurrent neural networks with time delays. First, two kinds of common activation functions appearing in the existing references are combined together and more general assumptions are given. To achieve our aim, a nonlinear delayed controller with two independent parameters different from the existing ones is provided, which leads to great difficulty. To overcome it, a newly developed inequality is used. Then, via Lyapunov function approach, some criteria are derived to guarantee the finite-time synchronization of the considered system, and the settling time for synchronization is also estimated. Finally, two numerical simulations are given to support the effectiveness and advantages of the obtained results.

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Robust synchronization of uncertain Markovian jump complex dynamical networks with time-varying delays and reaction–diffusion terms via sampled-data control

Cited by 43 publications

References 45 publications

Fault-tolerant anti-synchronization control for chaotic switched neural networks with time delay and reaction diffusion

Fault-tolerant anti-synchronization control for chaotic switched neural networks with time delay and reaction diffusion

Impulsive Synchronization Control Mechanism for Fractional-Order Complex-Valued Reaction-Diffusion Systems With Sampled-Data Control: Its Application to Image Encryption

Finite‐Time Synchronization for Complex‐Valued Recurrent Neural Networks with Time Delays

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