Prediction and identification of nonlinear dynamical systems using machine learning approaches

Jin, Leisheng; Liu, Zhuo; Li, Lijie

doi:10.1016/j.jii.2023.100503

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Prediction and identification of nonlinear dynamical systems using machine learning approaches

Leisheng Jin, Zhuo Liu, Lijie Li

Journal of Industrial Information Integration, Volume: 35, Start page: 100503

Swansea University Author: Lijie Li

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DOI (Published version): 10.1016/j.jii.2023.100503

Abstract

Nonlinear dynamical systems are widely implemented in many areas. The Prediction and identification of these dynamical systems purely based on observational data is of great significance for practical applications. In the work, we develop a machine learning based approach called Runge–Kutta guided n...

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Published in:	Journal of Industrial Information Integration
ISSN:	2452-414X
Published:	Elsevier BV 2023
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa63938
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first_indexed	2023-07-25T07:20:50Z
last_indexed	2023-07-25T07:20:50Z
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spelling	v2 63938 2023-07-25 Prediction and identification of nonlinear dynamical systems using machine learning approaches ed2c658b77679a28e4c1dcf95af06bd6 0000-0003-4630-7692 Lijie Li Lijie Li true false 2023-07-25 EEEG Nonlinear dynamical systems are widely implemented in many areas. The Prediction and identification of these dynamical systems purely based on observational data is of great significance for practical applications. In the work, we develop a machine learning based approach called Runge–Kutta guided next-generation reservoir computing (RKNG-RC). The proposed scheme can process data information generated by the most complicated nonlinear dynamical systems such as chaotic Lorenz63 system even with noise, and experimental systems such as chaotic Chua’s electronic circuit, showing an outstanding ability for prediction tasks. More importantly, the RKNG-RC is found to have distinctive interpretability that from the trained weights the ordinary differential equation governing the observable data can be deduced, which is beyond the processing capacities of traditional approaches. The work provides an efficient platform for processing information generated by various dynamical systems. Journal Article Journal of Industrial Information Integration 35 100503 Elsevier BV 2452-414X Prediction, Chaotic dynamical systems, Identification, Reservoir computing, Runge–Kutta 1 10 2023 2023-10-01 10.1016/j.jii.2023.100503 http://dx.doi.org/10.1016/j.jii.2023.100503 COLLEGE NANME Electronic and Electrical Engineering COLLEGE CODE EEEG Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University. STFC (ST/T006455/1). 2023-09-05T11:16:30.4636926 2023-07-25T08:19:03.5333076 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering Leisheng Jin 1 Zhuo Liu 2 Lijie Li 0000-0003-4630-7692 3 63938__28332__62798d285d984197a93be70a2891dc6b.pdf 63938.VOR.pdf 2023-08-21T12:06:05.3553608 Output 2637745 application/pdf Version of Record true Distributed under the terms of a Creative Commons Attribution CC-BY Licence. true eng http://creativecommons.org/licenses/by/4.0/
title	Prediction and identification of nonlinear dynamical systems using machine learning approaches
spellingShingle	Prediction and identification of nonlinear dynamical systems using machine learning approaches Lijie Li
title_short	Prediction and identification of nonlinear dynamical systems using machine learning approaches
title_full	Prediction and identification of nonlinear dynamical systems using machine learning approaches
title_fullStr	Prediction and identification of nonlinear dynamical systems using machine learning approaches
title_full_unstemmed	Prediction and identification of nonlinear dynamical systems using machine learning approaches
title_sort	Prediction and identification of nonlinear dynamical systems using machine learning approaches
author_id_str_mv	ed2c658b77679a28e4c1dcf95af06bd6
author_id_fullname_str_mv	ed2c658b77679a28e4c1dcf95af06bd6_***_Lijie Li
author	Lijie Li
author2	Leisheng Jin Zhuo Liu Lijie Li
format	Journal article
container_title	Journal of Industrial Information Integration
container_volume	35
container_start_page	100503
publishDate	2023
institution	Swansea University
issn	2452-414X
doi_str_mv	10.1016/j.jii.2023.100503
publisher	Elsevier BV
college_str	Faculty of Science and Engineering
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hierarchy_top_id	facultyofscienceandengineering
hierarchy_top_title	Faculty of Science and Engineering
hierarchy_parent_id	facultyofscienceandengineering
hierarchy_parent_title	Faculty of Science and Engineering
department_str	School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering
url	http://dx.doi.org/10.1016/j.jii.2023.100503
document_store_str	1
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description	Nonlinear dynamical systems are widely implemented in many areas. The Prediction and identification of these dynamical systems purely based on observational data is of great significance for practical applications. In the work, we develop a machine learning based approach called Runge–Kutta guided next-generation reservoir computing (RKNG-RC). The proposed scheme can process data information generated by the most complicated nonlinear dynamical systems such as chaotic Lorenz63 system even with noise, and experimental systems such as chaotic Chua’s electronic circuit, showing an outstanding ability for prediction tasks. More importantly, the RKNG-RC is found to have distinctive interpretability that from the trained weights the ordinary differential equation governing the observable data can be deduced, which is beyond the processing capacities of traditional approaches. The work provides an efficient platform for processing information generated by various dynamical systems.
published_date	2023-10-01T11:16:32Z
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score	11.016258

Prediction and identification of nonlinear dynamical systems using machine learning approaches

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