Finite-time anti-synchronization of memristive stochastic BAM neural networks with probabilistic time-varying delays

Manman Yuan; Weiping Wang; Xiong Luo; Linlin Liu; Wenbing Zhao

doi:10.1016/j.chaos.2018.06.013

Finite-time anti-synchronization of memristive stochastic BAM neural networks with probabilistic time-varying delays

Manman Yuan
, Weiping Wang
, Xiong Luo
, Linlin Liu
, Wenbing Zhao

Electrical and Computer Engineering

University of Science and Technology Beijing
Humboldt-University
Beijing University of Technology
Cleveland State University

Research output: Contribution to journal › Article › peer-review

35 Scopus citations

Abstract

This paper investigates the drive-response finite-time anti-synchronization for memristive bidirectional associative memory neural networks (MBAMNNs). Firstly, a class of MBAMNNs with mixed probabilistic time-varying delays and stochastic perturbations is first formulated and analyzed in this paper. Secondly, an nonlinear control law is constructed and utilized to guarantee drive-response finite-time anti-synchronization of the neural networks. Thirdly, by employing some inequality technique and constructing an appropriate Lyapunov function, some anti-synchronization criteria are derived. Finally, a number simulation is provided to demonstrate the effectiveness of the proposed mechanism.

Original language	English
Pages (from-to)	244-260
Number of pages	17
Journal	Chaos, Solitons and Fractals
Volume	113
DOIs	https://doi.org/10.1016/j.chaos.2018.06.013
State	Published - Aug 1 2018

Keywords

Finite-time anti-synchronization
Leakage delays
Memristor
Probabilistic time-varying delays
Stochastic BAM neural networks

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10.1016/j.chaos.2018.06.013

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title = "Finite-time anti-synchronization of memristive stochastic BAM neural networks with probabilistic time-varying delays",

abstract = "This paper investigates the drive-response finite-time anti-synchronization for memristive bidirectional associative memory neural networks (MBAMNNs). Firstly, a class of MBAMNNs with mixed probabilistic time-varying delays and stochastic perturbations is first formulated and analyzed in this paper. Secondly, an nonlinear control law is constructed and utilized to guarantee drive-response finite-time anti-synchronization of the neural networks. Thirdly, by employing some inequality technique and constructing an appropriate Lyapunov function, some anti-synchronization criteria are derived. Finally, a number simulation is provided to demonstrate the effectiveness of the proposed mechanism.",

keywords = "Finite-time anti-synchronization, Leakage delays, Memristor, Probabilistic time-varying delays, Stochastic BAM neural networks",

author = "Manman Yuan and Weiping Wang and Xiong Luo and Linlin Liu and Wenbing Zhao",

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T1 - Finite-time anti-synchronization of memristive stochastic BAM neural networks with probabilistic time-varying delays

AU - Yuan, Manman

AU - Wang, Weiping

AU - Luo, Xiong

AU - Liu, Linlin

AU - Zhao, Wenbing

PY - 2018/8/1

Y1 - 2018/8/1

N2 - This paper investigates the drive-response finite-time anti-synchronization for memristive bidirectional associative memory neural networks (MBAMNNs). Firstly, a class of MBAMNNs with mixed probabilistic time-varying delays and stochastic perturbations is first formulated and analyzed in this paper. Secondly, an nonlinear control law is constructed and utilized to guarantee drive-response finite-time anti-synchronization of the neural networks. Thirdly, by employing some inequality technique and constructing an appropriate Lyapunov function, some anti-synchronization criteria are derived. Finally, a number simulation is provided to demonstrate the effectiveness of the proposed mechanism.

AB - This paper investigates the drive-response finite-time anti-synchronization for memristive bidirectional associative memory neural networks (MBAMNNs). Firstly, a class of MBAMNNs with mixed probabilistic time-varying delays and stochastic perturbations is first formulated and analyzed in this paper. Secondly, an nonlinear control law is constructed and utilized to guarantee drive-response finite-time anti-synchronization of the neural networks. Thirdly, by employing some inequality technique and constructing an appropriate Lyapunov function, some anti-synchronization criteria are derived. Finally, a number simulation is provided to demonstrate the effectiveness of the proposed mechanism.

KW - Finite-time anti-synchronization

KW - Leakage delays

KW - Memristor

KW - Probabilistic time-varying delays

KW - Stochastic BAM neural networks

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JO - Chaos, Solitons and Fractals

JF - Chaos, Solitons and Fractals

ER -

Finite-time anti-synchronization of memristive stochastic BAM neural networks with probabilistic time-varying delays

Abstract

Keywords

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