Adaptive finite-time synchronization of stochastic mixed time-varying delayed memristor-based neural networks

This paper focuses on the finite-time synchronization of stochastic memristor-based neural networks with time-varying discrete and distributed delays and discontinuous nonlinear functions via the adaptive state-feedback controller. Based on the theories of set-valued mappings and stochastic differen...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 452; pp. 781 - 788
Main Authors Zhang, Tianliang, Deng, Feiqi
Format Journal Article
LanguageEnglish
Published Elsevier B.V 10.09.2021
Subjects
Adaptive control
Finite-time synchronization
Stochastic neural networks
Time-delay
Stochastic neural networks
Time-delay
Finite-time synchronization
Adaptive control
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Summary:This paper focuses on the finite-time synchronization of stochastic memristor-based neural networks with time-varying discrete and distributed delays and discontinuous nonlinear functions via the adaptive state-feedback controller. Based on the theories of set-valued mappings and stochastic differential inclusions, the finite-time synchronization of the drive neural network and response neural network is transformed into the finite-time stabilization problem of the corresponding error stochastic neural network. By choosing an appropriate Lyapunov function and employing the theory of stochastic finite-time stability, we present a method to design the control gain parameters. Finally, an example verifies the validity of the proposed method.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2019.09.117