Exponential synchronization of stochastic delayed memristive neural networks via a novel hybrid control.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date: Aug 4, 2020

Abstract

This paper investigates the exponential synchronization issue of stochastic delayed memristive neural networks (SDMNNs) via a novel hybrid control (HC), where impulsive instants are determined by the state-dependent trigger condition. The switching and quantification strategies are applied to the event-based impulsive controller to cope with the challenges induced concurrently by interval parameters, impulses, stochastic disturbance and time-varying delays. Furthermore, the control costs can be reduced and communication channels and bandwidths can be saved by using this designed controller. Then, novel Lyapunov functions and new analytical methods are constructed, which can be used to realize the exponential synchronization of SDMNNs via HC. Finally, a numerical simulation is provided to demonstrate our theoretical results.

Authors

  • Nijing Yang
    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China.
  • Yongbin Yu
    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China. Electronic address: ybyu@uestc.edu.cn.
  • Shouming Zhong
    School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China; Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China.
  • Xiangxiang Wang
    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China.
  • Kaibo Shi
    School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China. Electronic address: skbs111@163.com.
  • Jingye Cai
    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China.

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