Decentralized event-triggered synchronization of uncertain Markovian jumping neutral-type neural networks with mixed delays

Senan, Sibel; Ali, M. Syed; Vadivel, R.; Arik, Sabri

doi:10.1016/j.neunet.2016.10.003

Cited by 68 publications

(31 citation statements)

References 43 publications

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“…In realistic world, stochastic perturbations are unavoidable in the propagation of electric potential in a neuron and brings essential change to the neural networks [46]. Generally speaking, two types of stochastic perturbations are introduced into the analysis of stochastic complex dynamical networks (SCDNs): Markovian jumping parameters and finite dimensional Brownian motions, both of which have attracted much attention of researchers [18,19]. Proper control strategies are derived to synchronize the state trajectory of SCDNs [8,30,35].…”

Section: Introductionmentioning

confidence: 99%

Adaptive synchronization of stochastic complex dynamical networks and its application

Wei

Yao

Lin

et al. 2018

Neural Comput & Applic

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This paper investigates exponential synchronization for stochastic complex dynamical networks with reaction-diffusion terms and S-type distributed delays. Based on a generalized Halanay inequality and Poincaré inequality, adaptive control strategies for exponential synchronization are established by constructing a simple Lyapunov-Krasovskii functional candidate and utilizing the truncation method. Some numerical examples are provided to demonstrate the effectiveness of the obtained results. Finally, the proposed adaptive synchronization theoretical results are successfully applied to image encryption.

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Section: Introductionmentioning

confidence: 99%

Adaptive synchronization of stochastic complex dynamical networks and its application

Wei

Yao

Lin

et al. 2018

Neural Comput & Applic

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“…In addition, the weights matrices of the master system (1) and response system (4) are not the same before synchronization is realized. Therefore, compared to previous results [27,28], the research in this study is more complicated.…”

Section: Resultsmentioning

confidence: 77%

“…Ref. [28] considers the event-based synchronization of uncertain systems, in which the parameter uncertainties are always identical for the master and slave systems. In addition, the weights matrices of the master system (1) and response system (4) are not the same before synchronization is realized.…”

Section: Resultsmentioning

confidence: 99%

“…A type of discrete event-triggered controller has been proposed in [25], and ETC systems have been modeled as time-delayed systems. Since then, the ETC strategy has been extensively applied to research for synchronization [26][27][28][29][30] and state estimation [31][32][33]. A dynamic ETC approach [29] has been adopted to study the synchronization of discrete complex dynamical networks with time delays.…”

Section: Introductionmentioning

confidence: 99%

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Event-triggered hybrid impulsive control for synchronization of memristive neural networks

Zhang

Bao

2020

Sci. China Inf. Sci.

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This paper is concerned with the complete synchronization of memristive neural networks (MNNs) with time-varying delays. An event-triggered hybrid state feedback and impulsive controller is designed to save the limited system communication resources, and parameter mismatch is considered in the control design process. Based on the Lyapunov functional approach and the comparison principle for impulsive systems, a sufficient synchronization criterion is developed to derive the master MNN and response MNN. Additionally, under the event-triggered mechanism there exists a positive lower bound for inter-execution time, which implies the avoidance of Zeno behavior. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed synchronization design methods.

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“…It is worth noting that the dynamical behaviors of chaotic systems are highly sensitive to small changes of initial values and the trajectories in phase space are bounded due to the presence of nonlinearity. Therefore, the problem on chaos synchronization has been widely investigated and a large number of elegant results have been reported [2,3,6,8,10,11,13,14,[23][24][25][26]28,32,[36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Sampled-data synchronization of chaotic Lur’e systems via an adaptive event-triggered approach

Yuan

Fei

et al. 2018

Information Sciences

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In this paper, based on nonuniform sampling, the master-slave synchronization in a class of Lur'e systems is studied via using the sampled outputs of error system. Different from existing results, the transmission of control signals is determined by a novel adaptive event-triggered scheme, where the triggering thresholds depend on the dynamic behaviors of controlled systems rather than the predetermined constants as some traditional ones. Through choosing two augmented Lyapunov-Krasovskii functionals (LKFs), some delay-dependent synchronization criteria are formulated and the conservatism can be effectively reduced owing to the utilization of Wirtinger-based inequalities and delay-product-type LKF terms. Especially, the existence of the controller can be easily checked since the derived conditions are presented via LMI forms. Finally, two numerical examples with comparisons and simulations are given to illustrate the proposed results.

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Decentralized event-triggered synchronization of uncertain Markovian jumping neutral-type neural networks with mixed delays

Cited by 68 publications

References 43 publications

Adaptive synchronization of stochastic complex dynamical networks and its application

Adaptive synchronization of stochastic complex dynamical networks and its application

Event-triggered hybrid impulsive control for synchronization of memristive neural networks

Sampled-data synchronization of chaotic Lur’e systems via an adaptive event-triggered approach

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