Generating n-Scroll Chaotic Attractors From a Memristor-Based Magnetized Hopfield Neural Network

This brief presents a novel method to generate n-scroll chaotic attractors. First, a magnetized Hopfield neural network (HNN) with three neurons is modeled by introducing an improved multi-piecewise memristor to describe the effect of electromagnetic induction. Theoretical analysis and numerical simulation show that the memristor-based magnetized HNN can generate multi-scroll chaotic attractors with arbitrary number of scrolls. The number of scrolls can be easily changed by adjusting the memristor control parameters. Besides, complex initial offset boosting behavior is revealed from the magnetized HNN. Finally, a magnetized HNN circuit is designed and various typical attractors are verified.

Automated summary

This paper presents a novel method for generating n-scroll chaotic attractors. It models a magnetized Hopfield neural network (HNN) with three neurons by introducing an improved multi-piecewise memristor to describe the effect of electromagnetic induction. Theoretical analysis and numerical simulation demonstrate that the memristor-based magnetized HNN can generate multi-scroll chaotic attractors with any number of scrolls, which can be easily adjusted by controlling the memristor parameters. Additionally, complex initial offset boosting behavior is observed in the magnetized HNN. The designed magnetized HNN circuit is capable of generating various typical attractors.