Asymptotic synchronization for stochastic memristor-based neural networks with noise disturbance

Gao, Jie; Zhu, Ping; Xiong, Wenjun; Cao, Jinde; Zhang, Lin

doi:10.1016/j.jfranklin.2016.06.002

Cited by 55 publications

(27 citation statements)

References 30 publications

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“…On account of the previous works [19], we consider the following real-vauled memristor-based neural networks model:…”

Section: Model and Preliminariesmentioning

confidence: 99%

Exponential Stabilization of Time‐varying Delayed Complex‐valued Memristor‐based Neural Networks Via Impulsive Control

Fang

et al. 2018

Asian Journal of Control

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The exponential stabilization problem for a class of time-varying delayed complex-valued memristor-based neural networks via discontinuous impulsive control is investigated in this paper. Firstly, the time-varying delayed complex-valued memristor-based neural networks is translated to real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed to guarantee exponential stabilization of the complex-valued memristor-based neural networks with time-varying delays. Thirdly, by constructing an appropriate Lyapunov Krasovskii functional and adopting linear matrix inequality (LMI) technique, some sufficient exponential stabilization criteria are derived. Finally, an illustrative example is provided to illustrate the effectiveness of the obtained theoretical results.

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“…On account of the previous works [19], we consider the following real-vauled memristor-based neural networks model:…”

Section: Model and Preliminariesmentioning

confidence: 99%

Exponential Stabilization of Time‐varying Delayed Complex‐valued Memristor‐based Neural Networks Via Impulsive Control

Fang

et al. 2018

Asian Journal of Control

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“…Among many collective cooperative behaviors, synchronization has lots of valuable applications in different fields including secure communication, associative memory, information processing, biology systems, and systems control. () In recent years, many synchronization control methods have been proposed to synchronize two identical/different neural networks, including adaptive control, pinned control, impulsive control, feedback control,() intermittent control, and so on. For example, by designing state feedback controllers combined with delayed impulsive controller and adaptive controllers combined with delayed impulsive controller, Yang et al studied the pth moment synchronization for coupled memristive neural networks with discrete delays, unbounded distributed delays, and stochastic perturbations.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time synchronization of memristive neural networks with time‐varying delays via two control methods

Fang

2019

Math Methods in App Sciences

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In this paper, on the basis of the Lyapunov stability theory and finite‐time stability lemma, the finite‐time synchronization problem for memristive neural networks with time‐varying delays is studied by two control methods. First, the discontinuous state‐feedback control rule containing integral part for square sum of the synchronization error and the discontinuous adaptive control rule are designed for realizing synchronization of drive‐response memristive neural networks in finite time, respectively. Then, by using some important inequalities and defining suitable Lyapunov functions, some algebraic sufficient criteria guaranteeing finite‐time synchronization are deduced for drive‐response memristive neural networks in finite time. Furthermore, we give the estimation of the upper bounds of the settling time of finite‐time synchronization. Lastly, the effectiveness of the obtained sufficient criteria guaranteeing finite‐time synchronization is validated by simulation.

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“…Furthermore, in the biological neural system, the transfer of information among synapses always has stochastic perturbations, which come from the disturbance of the internal structure parameters, the errors caused by random measurement, external environment interference, etc. Therefore, the system model considering stochastic factors, which is called stochastic system, can describe the actual system more accurately and get extensive attention and research . Neural networks with stochastic perturbations and time‐varying delays have more richer dynamic properties, and the analytical method is more complex and more practical.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time synchronization for memristor‐based BAM neural networks with stochastic perturbations and time‐varying delays

Zhang

Peng

et al. 2018

Intl J Robust & Nonlinear

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Summary This paper focuses on the finite‐time synchronization issue of drive‐response memristor‐based bidirectional associative memory neural networks (MBAMNNs) with stochastic perturbations and time‐varying delays. Based on the mathematical model of memristor, definition of finite‐time stability for stochastic differential system and the drive‐response concept, some novel sufficient conditions are given to ensure the finite‐time synchronization of drive‐response MBAMNNs with stochastic perturbations and time‐varying delays. We design novel nonlinear feedback controllers to control the synchronization error to converge zero in a finite time, and the settling time for synchronization can be easily obtained. In addition, as two special cases, the finite‐time synchronization control criteria for bidirectional associative memory neural networks with stochastic perturbations and time‐varying delays and MBAMNNs without stochastic perturbations are also given. Finally, two numerical simulations are showed to demonstrate the correctness of main results.

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Asymptotic synchronization for stochastic memristor-based neural networks with noise disturbance

Cited by 55 publications

References 30 publications

Exponential Stabilization of Time‐varying Delayed Complex‐valued Memristor‐based Neural Networks Via Impulsive Control

Exponential Stabilization of Time‐varying Delayed Complex‐valued Memristor‐based Neural Networks Via Impulsive Control

Finite‐time synchronization of memristive neural networks with time‐varying delays via two control methods

Finite‐time synchronization for memristor‐based BAM neural networks with stochastic perturbations and time‐varying delays

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