Memristive Tabu learning neuron generated multi-wing attractor with FPGA implementation and application in encryption

Abstract

Memristors, with their unique nonlinear characteristics, are highly suitable for construction of novel neural models with rich dynamic behaviors. In this paper, a memristor with piecewise nonlinear state function is introduced into the Tabu learning neuron model, resulting in a novel memristive Tabu learning neuron model capable of generating a double wing chaotic butterfly. By modulating the state function of the memristor, we can effectively and easily alter the number of wings of the chaotic butterfly. Equilibrium points analysis further elucidates the mechanism behind the generation of multi-wing chaos. Various numerical simulation techniques, including phase portraits, bifurcation diagrams, Lyapunov exponent spectra, and local attraction basins, are employed to illustrate the dynamical behaviors of the proposed model. Moreover, the newly constructed neuron model is validated using FPGA hardware, with the results aligning with numerical simulations, thereby offering a dependable foundation for a memristor digital circuit based brain-like neuron model. Lastly, an image encryption application based on the multi-wing chaotic butterfly is developed to demonstrate the potential application of the model.