Exponential synchronization for stochastic neural networks with multi-delayed and Markovian switching via adaptive feedback control

Tong, Dongbing; Zhou, Wuneng; Zhou, Xianghui; Yang, Jun; Zhang, Liping; Xu, Yuhua

doi:10.1016/j.cnsns.2015.05.011

Cited by 69 publications

(18 citation statements)

References 19 publications

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“…For the neural network with Markovian jumping, many stability and synchronization results have been reported in the literature(see e.g. [9], [14]- [16], [29]- [31]). …”

Section: Introductionmentioning

confidence: 98%

Almost sure adaptive asymptotically synchronization for neutral-type multi-slave neural networks with Markovian jumping parameters and stochastic perturbation

et al. 2016

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“…For the neural network with Markovian jumping, many stability and synchronization results have been reported in the literature(see e.g. [9], [14]- [16], [29]- [31]). …”

Section: Introductionmentioning

confidence: 98%

Almost sure adaptive asymptotically synchronization for neutral-type multi-slave neural networks with Markovian jumping parameters and stochastic perturbation

et al. 2016

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“…Synchronization, which is the process in which two or more (coupled) dynamical systems seek to adjust a certain prescribed property of their motions to a common behavior in the limit as time tends to infinity [1], has attracted considerable attentions of numerous scientists from diverse fields including physics, biology, neuroscience, mathematics, chemistry and ecology, etc. And many different kinds of synchronization including complete synchronization, generalized synchronization, phase synchronization and cluster synchronization have been introduced [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Exponential synchronization of nonlinearly coupled complex networks with hybrid time-varying delays via impulsive control

Feng

Zhao

2016

Nonlinear Dyn

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This paper investigates the issue of exponential synchronization for a class of nonlinearly coupled complex networks with hybrid time-varying delays by applying impulsive control protocol. It should be mentioned we need not to assume the synchronization state since it may be difficult to find an appropriate function or dynamics to describe the synchronization manifold. By employing the Lyapunov method combined with average impulsive interval as well as the comparison principle, a set of simple and easily verifiable sufficient conditions are derived to guarantee the exponential synchronization for any initial values. Meanwhile, the exponential convergence rate can be specified through the analysis of the networks. Additionally, some numerical examples are given to verify the effectiveness of our theoretical results.

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“…Markovian jumping parameters often come from random factors such as sudden changes during the operation of the system, mutations in the connection of internal subsystems and the damage to system components. Therefore, Markovian jumping systems are studied, which provide the theoretical basis for solving engineering problems . The control for T‐S fuzzy Markovian jump systems was analyzed and researched .…”

Section: Introductionmentioning

confidence: 99%

Asynchronous control of T‐S fuzzy chaotic systems via a unified model using the hidden Markov model subject to strict dissipativity

Tong

Chen

et al. 2019

Optim Control Appl Methods

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Summary The asynchronous control for the unified model of chaotic systems with Markovian jumping parameters is considered via the Takagi‐Sugeno (T‐S) fuzzy approach in this paper. According to jumping modes for chaotic systems and modes for the asynchronous controller, a double‐mode process is generated. By introducing the hidden Markov model (HMM), the asynchronization phenomenon is represented between the controller and chaotic systems. By linear matrix inequalities (LMIs), some conditions are received to guarantee the stochastic stability and the strict dissipativity for chaotic systems. Based on the stochastic stability theory and the dissipativity theory, the asynchronous controller is discussed. The proposed method can be applied to the mode‐independent controller and the synchronous controller. Finally, the effectiveness of the proposed controller is illustrated by two examples.

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Exponential synchronization for stochastic neural networks with multi-delayed and Markovian switching via adaptive feedback control

Cited by 69 publications

References 19 publications

Almost sure adaptive asymptotically synchronization for neutral-type multi-slave neural networks with Markovian jumping parameters and stochastic perturbation

Almost sure adaptive asymptotically synchronization for neutral-type multi-slave neural networks with Markovian jumping parameters and stochastic perturbation

Exponential synchronization of nonlinearly coupled complex networks with hybrid time-varying delays via impulsive control

Asynchronous control of T‐S fuzzy chaotic systems via a unified model using the hidden Markov model subject to strict dissipativity

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