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A multiple local model learning for nonlinear and time-varying microwave heating process

A multiple local model learning for nonlinear and time-varying microwave heating process
A multiple local model learning for nonlinear and time-varying microwave heating process
This paper proposes a multiple local model learning approach for nonlinear and nonstationary microwave heating process (MHP). The proposed local learning framework performs model adaption at two levels: (1)~adaptation of the local linear model set, which adaptively partitions the process's data into multiple process states, each fitted with a local linear model; (2)~online adaptation of model prediction, which selects a subset of candidate local linear models and linearly combines them to produce the model prediction. Adaptive process state partition and fitting a new local linear model to the newly emerging process state is based on statistical hypothesis testing, and the optimal combining coefficients of the selected subset linear models are obtained by minimizing the mean square error with the constraint that the sum of these coefficients is unity. A case study involving a real-world industrial MHP is used to demonstrate the superior performance of the proposed multiple local model learning approach, in terms of online modeling accuracy and computational efficiency.
2161-4393
1-8
IEEE
Liu, Tong
e905fd5e-8652-401f-a00d-c98aa8cd835a
Liang, Shan
8fb6495d-342a-4519-9843-24e415cea8ca
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Liu, Tong
e905fd5e-8652-401f-a00d-c98aa8cd835a
Liang, Shan
8fb6495d-342a-4519-9843-24e415cea8ca
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Liu, Tong, Liang, Shan, Chen, Sheng and Harris, Chris (2019) A multiple local model learning for nonlinear and time-varying microwave heating process. In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE. pp. 1-8 . (doi:10.1109/IJCNN.2019.8851787).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper proposes a multiple local model learning approach for nonlinear and nonstationary microwave heating process (MHP). The proposed local learning framework performs model adaption at two levels: (1)~adaptation of the local linear model set, which adaptively partitions the process's data into multiple process states, each fitted with a local linear model; (2)~online adaptation of model prediction, which selects a subset of candidate local linear models and linearly combines them to produce the model prediction. Adaptive process state partition and fitting a new local linear model to the newly emerging process state is based on statistical hypothesis testing, and the optimal combining coefficients of the selected subset linear models are obtained by minimizing the mean square error with the constraint that the sum of these coefficients is unity. A case study involving a real-world industrial MHP is used to demonstrate the superior performance of the proposed multiple local model learning approach, in terms of online modeling accuracy and computational efficiency.

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ijcnn2019_19061 - Accepted Manuscript
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More information

Accepted/In Press date: 14 July 2019
Published date: 30 September 2019
Venue - Dates: International Joint Conference on Neural Networks, Hungary, 2019-07-14 - 2019-08-19

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Local EPrints ID: 433558
URI: http://eprints.soton.ac.uk/id/eprint/433558
ISSN: 2161-4393
PURE UUID: e713b000-6144-4b74-8753-eb00dd502b57

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Date deposited: 27 Aug 2019 16:30
Last modified: 06 Oct 2020 18:39

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