Two-dimensional non-autonomous neuron model with parameter-controlled multi-scroll chaotic attractors

Han Bao, Ruoyu Ding, Bei Chen, Quan Xu and Bocheng Bao

Chaos, Solitons & Fractals, 2023, vol. 169, issue C

Abstract: This work presents a two-dimensional (2-D) non-autonomous tabu learning single neuron (TLSN) model based on sinusoidal activation function (SAF), which can generate a class of multi-scroll chaotic attractors with parameters controlling the number of scrolls. The SAF-based TLSN (SAF-TLSN) model has a periodically alterable equilibrium point closely related to the model parameters. Its equilibrium trajectories with different stability types can be distributed in the phase plane, resulting in the generation of multi-scroll chaotic attractors. The multi-scroll chaotic attractors and their dynamical behaviors are investigated by phase plane portrait, maximal Lyapunov exponent, bifurcation diagram, and cross section. The numerical results demonstrate that each scroll of multi-scroll chaotic attractors is associated with the equilibrium trajectories composed of infinitely many unstable equilibrium points, and the scroll distributions of multi-scroll chaotic attractors are confined to the distribution ranges of equilibrium trajectories that are controlled by the model parameters. Besides, using a digital hardware platform, the SAF-TLSN model is implemented and the multi-scroll chaotic attractors with different scrolls are obtained experimentally to verify the numerical ones.

Keywords: Tabu learning single neuron (TLSN) model; Sinusoidal activation function (SAF); Equilibrium trajectory; Model parameter; Multi-scroll chaotic attractor; Experiment platform (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:169:y:2023:i:c:s0960077923001297

DOI: 10.1016/j.chaos.2023.113228

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