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

Yunfeng Liu, Zhiqiang Song and Manchun Tan

Mathematics and Computers in Simulation (MATCOM), 2019, vol. 159, issue C, 1-17

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.

Keywords: Multistability; μ-stability; Multiperiodicity; Memristor-based fuzzy cellular neural networks; Nonmonotonic activation functions (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:159:y:2019:i:c:p:1-17

DOI: 10.1016/j.matcom.2018.10.007

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