Harnessing machine learning for identifying parameters in fractional chaotic systems

Ce Liang, Weiyuan Ma, Chenjun Ma and Ling Guo

Applied Mathematics and Computation, 2025, vol. 500, issue C

Abstract: This paper investigates data-driven learning techniques for fractional chaotic systems (FCS), specifically those utilizing the Caputo derivative. Three machine learning (ML) methods are employed for parameter estimation: feedforward neural networks (FNN), long short-term memory (LSTM), and gated recurrent units (GRU). Optimization problems are formulated, and the well-known algorithms, Backpropagation Through Time (BPTT) and Adam, are employed to train the weights and parameters of the ML models. Systematic numerical testing reveals that LSTM demonstrates superior recognition performance for undisturbed data, while GRU achieves higher accuracy in the presence of disturbances. This study presents a highly accurate approach for solving parameter inverse problems, with the potential for extending these methods to other fractional systems.

Keywords: Chaos system; Caputo fractional derivative; Parameter estimation; Machine learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S009630032500181X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:500:y:2025:i:c:s009630032500181x

DOI: 10.1016/j.amc.2025.129454

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().