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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2018

15.07.2017

Finite-Time Robust Synchronization of Memrisive Neural Network with Perturbation

verfasst von: Hui Zhao, Lixiang Li, Haipeng Peng, Jürgen Kurths, Jinghua Xiao, Yixian Yang

Erschienen in: Neural Processing Letters | Ausgabe 2/2018

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Abstract

In this paper, we study finite-time synchronization of a memristive neural network (MNN) with impulsive effect and stochastic perturbation. Because the parameters of the MNN are state-dependent, the traditional analytical method and control technique can not be directly used. In previous research, differential inclusions theory and set-valued mappings technique have been recently introduced to deal with this MNN system. But, we study the synchronization of MNN without using the previous solution technique. A novel analytical technique is first proposed to transform the MNN to a class of neural network (cNN) with uncertain parameters. The finite-time synchronization is obtained by disposing of parameter mismatch, impulsive effect or stochastic perturbation for the cNN. Several useful criteria of synchronization are obtained based on Lyapunov function, linear matrix inequality (LMI) and finite-time stability theory. Finally, two examples are given to demonstrate the effectiveness of our proposed method.
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Metadaten
Titel
Finite-Time Robust Synchronization of Memrisive Neural Network with Perturbation
verfasst von
Hui Zhao
Lixiang Li
Haipeng Peng
Jürgen Kurths
Jinghua Xiao
Yixian Yang
Publikationsdatum
15.07.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9664-9

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