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 language | English |
|---|---|
| Article number | 115213 |
| Journal | Chaos, Solitons and Fractals |
| Volume | 186 |
| Online published | 6 Jul 2024 |
| DOIs | |
| Publication status | Published - 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/