Two-dimensional non-autonomous neuron model with parameter-controlled multi-scroll chaotic attractors. (April 2023) - Bao, Han; Ding, Ruoyu; Chen, Bei; Xu, Quan; Bao, Bocheng
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. Highlights: A 2-D non-autonomous SAF-TLSN model with multi-scroll chaotic attractors is presented. The generation of multi-scroll chaotic attractors is studied by their equilibrium trajectories. Multi-scroll chaotic attractors controlled by model parameters are investigated numerically. The SAF-TLSN model is implemented using a digital platform to verify numerical results.