Sampled-Data Synchronization of Stochastic Markovian Jump Neural Networks With Time-Varying Delay

Chen, Guoliang; Xia, Jianwei; Park, Ju H.; Shen, Hao; Zhuang, Guangming

doi:10.1109/tnnls.2021.3054615

Cited by 93 publications

(17 citation statements)

References 64 publications

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“…Based on constructing mode-dependent one-sided and two-sided looped-functionals, the random sampled-data synchronization problem of MJNNs was studied in [34] and Ref. [34] concluded that two-sided looped-functionals has lower conservative. The H∞ synchronization problems of MJNNs were studied by constructing a composite disturbance rejection controller and a mode-dependent synchronous controller in [35,36], respectively.…”

Section: Introductionmentioning

confidence: 99%

Sampled-Data Exponential Synchronization of Markovian Jump Chaotic Lur'e Systems with Multiple Time Delays

Yang

Wang

Huang

et al. 2022

Preprint

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This paper studies the mean square exponential synchronization control of the Markovian jump chaotic Lur'e systems (MJCLSs) with multiple time delays via aperiodic sampling. Firstly, by introducing the mode-dependent single/double integral terms and considering the system state at the sampling instant, a new mode-dependent Lyapunov functional based on the looped-functional is constructed. On the basis of the constructed functional and by estimating the expectation of the solution of MJCLSs with multiple time delays, an exponential synchronization criterion in the mean square sense is presented. Then, based on the obtained stability conditions, a design method for aperiodic sampled-data controller (ASDC) to ensure the exponential synchronization is proposed. Finally, the effectiveness of the developed method is demonstrated by the Chua’s circuit model and a neural network model. Compared with the existing literature, an admissible larger upper bound of sampling period can be obtained.

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Section: Introductionmentioning

confidence: 99%

Sampled-Data Exponential Synchronization of Markovian Jump Chaotic Lur'e Systems with Multiple Time Delays

Yang

Wang

Huang

et al. 2022

Preprint

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“…Under the framework of loop‐based Lyapunov functional (LBLF) approach, sampled‐data synchronization problem for stochastic Markovian jump neural networks was considered in Ref. [39], via constructing mode‐dependent one‐sided and two‐sided LBLF for comparison, and it turned out that two‐sided LBLF is able to obtain smaller conservativeness results. However, as far as our knowledge, few studies have discussed the problem of sampled‐data synchronization with respect to SMJSs, which is another motivation for this paper, and it further explores the LBLF approach by constructing tailored one‐sided and two‐sided LBLF.…”

Section: Introductionmentioning

confidence: 99%

Sampled‐data synchronization of singular Markovian jump system: Application to DC motor model

Wang

Chen

Yang

et al. 2022

IET Cyber-Phy Sys Theory & Ap

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Sampled-data synchronization problem for singular Markovian jump systems (SMJSs) subject to aperiodic sampled-data control is investigated. Firstly, via constructing modedependent one-sided loop-based Lyapunov functional (LBLF) and two-sided LBLF, two different stochastically admissible conditions are suggested for error SMJSs with aperiodic sampled-data. It is guaranteed that the slave system is stochastically synchronized to the master system on the basis of the proposed stochastically admissible conditions. Secondly, two corresponding mode-dependent aperiodic sampled-data controller design approaches are provided for error SMJSs based on two different conditions, separately. Finally, the validity of these approaches is demonstrated by a direct current (DC) motor model. It also demonstrated that the two-sided LBLF method possesses a larger upper bound of sampled-data period than the one-sided LBLF method.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Markovian jump systems (MJSs) have attracted much attention in the past few decades, owing to their well descriptions for the practical systems, such as truck-trailer systems and mass-spring system, which may be subject to unpredictable variations in their structures, possibly caused by sudden environment disturbance, random failures of the components and so on. [1][2][3][4] Close attention has been paid to the filter design and stability analysis for MJSs and many important results have been appeared. [5][6][7][8] The authors in Reference 5 studied the problem of stabilization and H ∞ performance of event-triggered semi-Markovian systems with actuator saturation.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time adaptive event‐triggered asynchronous state estimation for Markov jump systems with cyber‐attacks

Zha

Liao

Liu

et al. 2021

Intl J Robust & Nonlinear

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This article considers the issue of finite‐time asynchronous state estimation for event‐triggered nonlinear Markovian jump systems subject to cyber‐attacks. An adaptive event‐triggered scheme is introduced to cope with the capacity constraint of the networked resources. It is assumed that the transmitted sensor measurements may experience randomly malicious cyber‐attacks. Considering the effect of the adaptive event‐triggered scheme and the occurrence of cyber‐attacks, we establish a new state estimation error system model. Sufficient conditions of the finite‐time boundedness and the finite‐time boundedness are developed for the augmented system. Then, the design methods of the asynchronous estimator gains are derived, which can ensure the finite‐time boundedness of the estimation error system. Finally, a numerical example is given to show the effectiveness of the theoretical results.

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Sampled-Data Synchronization of Stochastic Markovian Jump Neural Networks With Time-Varying Delay

Cited by 93 publications

References 64 publications

Sampled-Data Exponential Synchronization of Markovian Jump Chaotic Lur'e Systems with Multiple Time Delays

Sampled-Data Exponential Synchronization of Markovian Jump Chaotic Lur'e Systems with Multiple Time Delays

Sampled‐data synchronization of singular Markovian jump system: Application to DC motor model

Finite‐time adaptive event‐triggered asynchronous state estimation for Markov jump systems with cyber‐attacks

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