Exponential synchronization of memristive neural networks with time-varying delays via quantized sliding-mode control.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date: Mar 24, 2020

Abstract

In the paper, exponential synchronization issue is considered for memristive neural networks (MNNs) with time-varying delays via quantized sliding-mode algorithm. Quantized Sliding-mode controller is introduced to ensure the slave system can be exponentially synchronized with the host system via the super-twisting algorithm, which has been proved in the main results. Quantization function consists of uniform quantizer and logarithmic quantizer. Simulation results are given with comparisons between two quantizers in the end.

Authors

  • Bo Sun
    College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China. Electronic address: tosunbo@bnu.edu.cn.
  • Shengbo Wang
    School of Automation and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China. Electronic address: 1026848587@qq.com.
  • Yuting Cao
    School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China.
  • Zhenyuan Guo
    College of Mathematics and Econometrics, Hunan University, Changsha, Hunan 410082, PR China; Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong. Electronic address: zyguo@hnu.edu.cn.
  • Tingwen Huang
  • Shiping Wen

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