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Multistability of Delayed Hybrid Impulsive Neural Networks

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

Abstract

The important topic of multistability of continuous- and discrete-time neural network models has been investigated rather extensively. Concerning the design of associative memories, this chapter introduces the multistability of delayed hybrid impulsive neural networks and lays emphasis on the impulse effect. Arising from the spikes in biological networks, impulsive neural networks provide an efficient model for synaptic interconnections among neurons. Using state-space decomposition, the coexistence of multiple equilibria of hybrid impulsive neural networks is analyzed. Multistability criteria are then established regrading delayed hybrid impulsive neurodynamics, for which both the impulse effect on convergence rate and the basin of attraction of equilibria are discussed. Illustrative examples are given to verify the theoretical results and demonstrate an application to the design of associative memories. It is shown by an experimental example that delayed hybrid impulsive neural networks have the advantages of high storage capacity and high fault tolerance when used for associative memories.

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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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