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Deep cascade gradient RBF networks with output-relevant feature extraction and adaptation for nonlinear and nonstationary processes

Deep cascade gradient RBF networks with output-relevant feature extraction and adaptation for nonlinear and nonstationary processes
Deep cascade gradient RBF networks with output-relevant feature extraction and adaptation for nonlinear and nonstationary processes
The main challenge for industrial predictive models is how to effectively deal with big data from high-dimensional processes with nonstationary characteristics. Although deep networks, such as the stacked autoencoder (SAE), can learn useful features from massive data with multilevel architecture, it is difficult to adapt them online to track fast time-varying process dynamics. To integrate feature learning and online adaptation, this paper proposes a deep cascade gradient radial basis function (GRBF) network for online modeling and prediction of nonlinear and nonstationary processes. The proposed deep learning method consists of three modules. First, a preliminary prediction result is generated by a GRBF weak predictor, which is further combined with raw input data for feature extraction. By incorporating the prior weak prediction information, deep output-relevant features are extracted using a SAE. Online prediction is finally produced upon the extracted features with a GRBF predictor, whose weights and structure are updated online to capture fast time-varying process characteristics. Three real-world industrial case studies demonstrate that the proposed deep cascade GRBF network outperforms existing state-of-the-art online modeling approaches as well as deep networks, in terms of both online prediction accuracy and computational complexity.
2168-2267
Liu, Tong
17f1a70b-449d-4078-af64-957a5b374698
Tian, Zeyue
2953127a-67fd-4980-81e3-d7f68f59092c
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Wang, Kai
3acbc899-cb32-4885-876f-d7b353577dfb
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d
Liu, Tong
17f1a70b-449d-4078-af64-957a5b374698
Tian, Zeyue
2953127a-67fd-4980-81e3-d7f68f59092c
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Wang, Kai
3acbc899-cb32-4885-876f-d7b353577dfb
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d

Liu, Tong, Tian, Zeyue, Chen, Sheng, Wang, Kai and Harris, Chris J. (2022) Deep cascade gradient RBF networks with output-relevant feature extraction and adaptation for nonlinear and nonstationary processes. IEEE Transactions on Cybernetics. (In Press)

Record type: Article

Abstract

The main challenge for industrial predictive models is how to effectively deal with big data from high-dimensional processes with nonstationary characteristics. Although deep networks, such as the stacked autoencoder (SAE), can learn useful features from massive data with multilevel architecture, it is difficult to adapt them online to track fast time-varying process dynamics. To integrate feature learning and online adaptation, this paper proposes a deep cascade gradient radial basis function (GRBF) network for online modeling and prediction of nonlinear and nonstationary processes. The proposed deep learning method consists of three modules. First, a preliminary prediction result is generated by a GRBF weak predictor, which is further combined with raw input data for feature extraction. By incorporating the prior weak prediction information, deep output-relevant features are extracted using a SAE. Online prediction is finally produced upon the extracted features with a GRBF predictor, whose weights and structure are updated online to capture fast time-varying process characteristics. Three real-world industrial case studies demonstrate that the proposed deep cascade GRBF network outperforms existing state-of-the-art online modeling approaches as well as deep networks, in terms of both online prediction accuracy and computational complexity.

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DGRBFlv_re3 - Accepted Manuscript
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Accepted/In Press date: 12 February 2022

Identifiers

Local EPrints ID: 454982
URI: http://eprints.soton.ac.uk/id/eprint/454982
ISSN: 2168-2267
PURE UUID: 74d90992-a7cf-4b7b-ac89-b4f43e829c4a

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Date deposited: 03 Mar 2022 17:35
Last modified: 18 Jul 2022 16:54

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Contributors

Author: Tong Liu
Author: Zeyue Tian
Author: Sheng Chen
Author: Kai Wang
Author: Chris J. Harris

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