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Selective ensemble of multiple local model learning for nonlinear and nonstationary systems

Selective ensemble of multiple local model learning for nonlinear and nonstationary systems
Selective ensemble of multiple local model learning for nonlinear and nonstationary systems
This paper proposes a selective ensemble of multiple local model learning for modeling and identification of nonlinear and nonstationary systems, in which the set of local linear models are self adapted to capture the newly emerging process characteristics and the prediction of the process output is also self adapted based on an optimally selected ensemble of subset linear local models. Specifically, our selective ensemble of multiple local model learning approach performs the model adaptation at two levels. At the level of local model adaptation, a newly emerging process state in the incoming data is automatically identified and a new local linear model is fitted to this newly emerged process state. At the level of on- line prediction, a subset of candidate local linear models are optimally selected and the prediction of the process output is computed as an optimal linear combiner of the selected subset local linear models. Two case studies involving chaotic time series prediction and modeling of a real-world industrial microwave heating process are used to demonstrate the effectiveness of our proposed approach, in comparison with other existing methods for modeling and identification of nonlinear and time-varying systems.
Local model learning, Nonlinear and time-varying system, Online modeling and prediction, Selective ensemble
0925-2312
98-111
Liu, Tong
e905fd5e-8652-401f-a00d-c98aa8cd835a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Liang, Shan
8fb6495d-342a-4519-9843-24e415cea8ca
Harris, Chris J.
daa59e88-2e26-42df-bf2e-f8e6792ecb18
Liu, Tong
e905fd5e-8652-401f-a00d-c98aa8cd835a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Liang, Shan
8fb6495d-342a-4519-9843-24e415cea8ca
Harris, Chris J.
daa59e88-2e26-42df-bf2e-f8e6792ecb18

Liu, Tong, Chen, Sheng, Liang, Shan and Harris, Chris J. (2020) Selective ensemble of multiple local model learning for nonlinear and nonstationary systems. Neurocomputing, 378, 98-111. (doi:10.1016/j.neucom.2019.10.015).

Record type: Article

Abstract

This paper proposes a selective ensemble of multiple local model learning for modeling and identification of nonlinear and nonstationary systems, in which the set of local linear models are self adapted to capture the newly emerging process characteristics and the prediction of the process output is also self adapted based on an optimally selected ensemble of subset linear local models. Specifically, our selective ensemble of multiple local model learning approach performs the model adaptation at two levels. At the level of local model adaptation, a newly emerging process state in the incoming data is automatically identified and a new local linear model is fitted to this newly emerged process state. At the level of on- line prediction, a subset of candidate local linear models are optimally selected and the prediction of the process output is computed as an optimal linear combiner of the selected subset local linear models. Two case studies involving chaotic time series prediction and modeling of a real-world industrial microwave heating process are used to demonstrate the effectiveness of our proposed approach, in comparison with other existing methods for modeling and identification of nonlinear and time-varying systems.

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Selective ensemble of multiple local model learning for nonlinear and nonstationary systems - Accepted Manuscript
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More information

Accepted/In Press date: 11 October 2019
e-pub ahead of print date: 17 October 2019
Published date: 22 February 2020
Keywords: Local model learning, Nonlinear and time-varying system, Online modeling and prediction, Selective ensemble

Identifiers

Local EPrints ID: 436254
URI: http://eprints.soton.ac.uk/id/eprint/436254
ISSN: 0925-2312
PURE UUID: 89932797-c6a2-401f-a170-7270fffbc3f1

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Date deposited: 04 Dec 2019 17:30
Last modified: 11 Oct 2020 04:01

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Contributors

Author: Tong Liu
Author: Sheng Chen
Author: Shan Liang
Author: Chris J. Harris

University divisions

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