Offset Boosting-Oriented Construction of Multi-Scroll Attractor via a Memristor Model

Abstract

The static architecture of artificial neural networks has fixed synaptic weights, whose connections do not change according to new information or learning experience. In contrast, the capacity of synaptic weight empowers biological neural networks to learn and adapt to diverse tasks, resulting in various dynamical behaviors. In this paper, a novel memristor model is designed into the Hopfield neural network for generating any desired number of multi-scroll attractors. Offset booster provides a channel for distance regulation and number control of coexisting attractors. Independent offset boosters determine the coexisting patterns including the types of one-scroll attractor, two-scroll attractor, four-scroll attractor, and other mixed types. In addition, the digital circuit platform of CH32V307 is applied to verify numerical simulations. Finally, the chaotic data generated in the memristive Hopfield neural network is introduced into the northern goshawk optimization (MHNN-NGO), by which the full network optimization is achieved.