Impulsive synchronization of coupled delayed neural networks with actuator saturation and its application to image encryption.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date: May 15, 2020

Abstract

The actuator of any physical control systems is constrained by amplitude and energy, which causes the control systems to be inevitably affected by actuator saturation. In this paper, impulsive synchronization of coupled delayed neural networks with actuator saturation is presented. A new controller is designed to introduce actuator saturation term into impulsive controller. Based on sector nonlinearity model approach, impulsive controls with actuator saturation and with partial actuator saturation are studied, respectively, and some effective sufficient conditions are obtained. Numerical simulation is presented to verify the validity of the theoretical analysis results. Finally, the impulsive synchronization is applied to image encryption. The experimental results show that the proposed image encryption system has high security properties.

Authors

  • Deqiang Ouyang
    Center for Future Media, School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China. Electronic address: ouyangdeqiang@std.uestc.edu.cn.
  • Jie Shao
    Center for Future Media, School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Sichuan Artificial Intelligence Research Institute, Yibin 644000, China. Electronic address: shaojie@uestc.edu.cn.
  • Haijun Jiang
    College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, Xinjiang, PR China. Electronic address: jianghaijunxju@163.com.
  • Sing Kiong Nguang
    Department of Electrical and Computer Engineering, The University of Auckland, Auckland 1142, New Zealand. Electronic address: sk.nguang@auckland.ac.nz.
  • Heng Tao Shen
    Center for Future Media, School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Sichuan Artificial Intelligence Research Institute, Yibin 644000, China. Electronic address: shenhengtao@uestc.edu.cn.

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