2024/05/13 by Marius‐F. Danca, Guanrong Chen, Danca, Marius-F. +1
Agricultural and Biological Sciences · Computer Science · #Advanced Scientific Research Methods #Chaotic Dynamics (nlin.CD) #FOS: Physical sciences #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.2405.07567
openalex publication_date 2024/05/13 · openalex created_date 2024/05/15 · openalex updated_date 2026/07/28
In this paper, the Parameter Switching (PS) algorithm is used to approximate numerically attractors of a Hopfield Neural Network (HNN) system. The PS algorithm is a convergent scheme designed for approximating attractors of an autonomous nonlinear system, depending linearly on a real parameter. Aided by the PS algorithm, it is shown that every attractor of the HNN system can be expressed as a convex combination of other attractors. The HNN system can easily be written in the form of a linear parameter dependence system, to which the PS algorithm can be applied. This work suggests the possibility to use the PS algorithm as a control-like or anticontrol-like method for chaos.