Approximation and decomposition of attractors of a Hopfield neural network system

Marius-F. Danca and Guanrong Chen

Chaos, Solitons & Fractals, 2024, vol. 186, issue C

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.

Keywords: Hopfield neural network system; Parameter switching algorithm; Numerical attractor; Attractors approximation; Attractor decomposition (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:186:y:2024:i:c:s0960077924007653

DOI: 10.1016/j.chaos.2024.115213

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