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

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

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	American English
Journal	Chaos Solitons and Fractals
Volume	133
DOIs	https://doi.org/10.1016/j.chaos.2018.06.013
State	Published - Aug 1 2018

Keywords

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

Disciplines

Electrical and Computer Engineering

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Finite-time Anti-synchronization of Memristive Stochastic BAM NeuFinal published version, 3.22 MBLicense: CC BY

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

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

year = "2018",

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doi = "10.1016/j.chaos.2018.06.013",

language = "American English",

volume = "133",

journal = "Chaos Solitons and Fractals",

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

UR - https://engagedscholarship.csuohio.edu/enece_facpub/451

U2 - 10.1016/j.chaos.2018.06.013

DO - 10.1016/j.chaos.2018.06.013

M3 - Article

VL - 133

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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