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Hybrid Memristor-Based Impulsive Neural Networks

  • Zhi-Hong Guan
  • Bin Hu
  • Xuemin (Sherman) Shen
Chapter

Abstract

This chapter introduces a class of heterogeneous delayed impulsive neural networks with memristors and focuses on their collective evolution for multisynchronization. The multisynchronization represents a diversified collective behavior that is inspired by multitasking as well as observations of heterogeneity and hybridity arising from system models. In view of memristor, the memristor-based impulsive neural network is first represented by an impulsive differential inclusion. According to the memristive and impulsive mechanism, a fuzzy logic rule is introduced, and then a new fuzzy hybrid impulsive and switching control method is presented correspondingly. It is shown that using the proposed fuzzy hybrid control scheme, multisynchronization of interconnected memristor-based impulsive neural networks can be guaranteed with a positive exponential convergence rate. The heterogeneity and hybridity in system models thus can be indicated by the obtained error thresholds that contribute to the multisynchronization. Numerical examples are presented and compared to demonstrate the effectiveness of the developed theoretical results.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhi-Hong Guan
    • 1
  • Bin Hu
    • 2
  • Xuemin (Sherman) Shen
    • 3
  1. 1.College of AutomationHuazhong University of Science and TechnologyWuhanChina
  2. 2.Wuhan National Laboratory For OptoelectronicsHuazhong University of Science and TechnologyWuhanChina
  3. 3.Electrical and Computer Engineering DepartmentUniversity of WaterlooWaterlooCanada

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