Approximation and decomposition of attractors of a Hopfield neural network system

Marius-F. Danca*, Guanrong Chen

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Abstract

In this paper, the Parameter Switching (PS) algorithm is used to numerically approximate attractors of a Hopfield Neural Network (HNN) system. The PS algorithm is a convergent scheme designed for approximating the 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. © 2024 Elsevier Ltd
Original languageEnglish
Article number115213
JournalChaos, Solitons and Fractals
Volume186
Online published6 Jul 2024
DOIs
Publication statusPublished - Sept 2024

Research Keywords

  • Attractor decomposition
  • Attractors approximation
  • Hopfield neural network system
  • Numerical attractor
  • Parameter switching algorithm

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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