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Hidden Multistability in a Memristor‐Based Cellular Neural Network

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Listed:
  • Birong Xu
  • Hairong Lin
  • Guangyi Wang

Abstract

In this paper, we report a novel memristor‐based cellular neural network (CNN) without equilibrium points. Dynamical behaviors of the memristor‐based CNN are investigated by simulation analysis. The results indicate that the system owns complicated nonlinear phenomena, such as hidden attractors, coexisting attractors, and initial boosting behaviors of position and amplitude. Furthermore, both heterogeneous multistability and homogenous multistability are found in the CNN. Finally, Multisim circuit simulations are performed to prove the chaotic characteristics and multistability of the system.

Suggested Citation

  • Birong Xu & Hairong Lin & Guangyi Wang, 2020. "Hidden Multistability in a Memristor‐Based Cellular Neural Network," Advances in Mathematical Physics, John Wiley & Sons, vol. 2020(1).
    • Handle: RePEc:wly:jnlamp:v:2020:y:2020:i:1:n:9708649
    DOI: 10.1155/2020/9708649
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    References listed on IDEAS

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    1. Lin, Hairong & Wang, Chunhua, 2020. "Influences of electromagnetic radiation distribution on chaotic dynamics of a neural network," Applied Mathematics and Computation, Elsevier, vol. 369(C).
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