@conference{10902/36302,
year = {2024},
url = {https://hdl.handle.net/10902/36302},
abstract = {Reconstructing the attractors of unknown chaotic systems from time series data presents a formidable challenge with broad applications across various disciplines. In this paper, we propose a swarm intelligence approach to address this challenge, focusing specifically on low-dimensional chaotic maps. Our approach is based on the bat algorithm, a renowned bio-inspired optimization technique well-suited for continuous optimization tasks. We evaluate the effectiveness and validity of our proposed approach by applying it to two distinct examples of chaotic maps: the Burger map and the Duffing map. Through comprehensive experimentation, we showcase the satisfactory performance of our method in reconstructing attractors from time series data. Based on our empirical findings, we conclude that our approach holds significant promise for the reconstruction of attractors of low-dimensional chaotic maps using time series data.},
organization = {Research work funded by Project PDE-GIR (No. 778035) of the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions, and project PID2021-127073OB-I00 of the MCIN/AEI/10.13039/501100011033/FEDER,EU, Spanish Ministry of Science and Innovation.},
publisher = {Institute of Electrical and Electronics Engineers, Inc.},
publisher = {IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Osaka, Japan, 2024, 1642-1647},
title = {Bat algorithm for attractor reconstruction of low-dimensional chaotic maps from time series},
author = {Gálvez Tomida, Akemi and Pérez Carabaza, Sara and Iglesias Prieto, Andrés},
}