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Multiple μ-stability and multiperiodicity of delayed memristor-based fuzzy cellular neural networks with nonmonotonic activation functions

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  • Liu, Yunfeng
  • Song, Zhiqiang
  • Tan, Manchun

Abstract

In this paper, the multistability and multiperiodicity problems are investigated for the memristor-based fuzzy cellular neural networks (MFCNNs) with nonmonotonic activation functions and unbounded time-varying delays. Based on the fixed point theorem and the geometrical properties of activation functions, sufficient criteria are obtained to ensure such n-neuron MFCNNs can have at least ∏i=1n(2Ki+1) equilibrium points with Ki>0 in which ∏i=1n(Ki+1) are locally μ-stable. As an extension of the theory, the existence of ∏i=1n(Ki+1) locally exponentially stable periodic solutions with time-periodic inputs is also derived. Finally, one example is presented to confirm our results.

Suggested Citation

  • Liu, Yunfeng & Song, Zhiqiang & Tan, Manchun, 2019. "Multiple μ-stability and multiperiodicity of delayed memristor-based fuzzy cellular neural networks with nonmonotonic activation functions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 159(C), pages 1-17.
    • Handle: RePEc:eee:matcom:v:159:y:2019:i:c:p:1-17
    DOI: 10.1016/j.matcom.2018.10.007
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    References listed on IDEAS

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    Cited by:

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    3. Liu, Yang & Wang, Zhen & Huang, Xia, 2022. "Multistability analysis of state-dependent switched Hopfield neural networks with the Gaussian-wavelet-type activation function," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 196(C), pages 232-250.

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