Dynamics analysis, hardware implementation and engineering applications of novel multi-style attractors in a neural network under electromagnetic radiation

This article studies the interesting dynamics of a small neural network with three neurons under electromagnetic radiation. The electromagnetic radiation strength can change the number of the equilibrium points in the neural network, which leads to the diversification of the attractor's trajectory. Thus, the novel multi-style attractors like one-to-two-spiral attractors and one-to-four-scroll attractors can be generated from the neural network stimulated by electromagnetic radiation. Besides, the plentiful dynamical behaviors are observed in the neural network, such as transitional coexisting attractors, hypogenetic at tractors, periodic patterns, firing patterns, transient chaos and intermittent chaos. In terms of hardware implementation, we utilize FPGA to digitally implement the constructed neural network model. The experimental verification results are highly consistent with the numerical simulation results. In the aspects of engineering application, we apply it to pseudo-random number generator and image encryption respectively, test the random performance of different chaotic attractors by NIST test suite, describe the image encryption scheme based on neural network and estimate its security performances. The ultimate outcomes demonstrate that the neural network model with chaotic behavior has superior randomness and wonderful security, which is very suitable for engineering applications based on chaos. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study investigates the dynamics of a small neural network with three neurons under electromagnetic radiation, revealing that the strength of the radiation can alter the equilibrium points in the network and lead to diverse attractor trajectories. Various dynamic behaviors were observed, including transitional coexisting attractors, chaos, and intermittent chaos.