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Multi-output selective ensemble identification of nonlinear and nonstationary industrial processes

Multi-output selective ensemble identification of nonlinear and nonstationary industrial processes
Multi-output selective ensemble identification of nonlinear and nonstationary industrial processes
A key characteristic of biological systems is the ability to update the memory by learning new knowledge and removing out-of-date knowledge so that intelligent decision can be made based on the relevant knowledge acquired in the memory. Inspired by this fundamental biological principle, this paper proposes a multi-output selective ensemble regression (SER) for online identification of multi-output nonlinear time-varying industrial processes. Specifically, an adaptive local learning approach is developed to automatically identify and encode newly emerging process state by fitting a local multi-output linear model based on the multi-output hypothesis testing. This growing strategy ensures a highly diverse and independent local model set. The online modeling is constructed as a multi-output SER predictor by optimizing the combining weights of the selected local multi-output models based on a probability metric. An effective pruning strategy is also developed to remove the unwanted out-of-date local multi-output linear models in order to achieve low online computational complexity without scarifying the prediction accuracy. A simulated two-output process and two real-world identification problems are used to demonstrate the effectiveness of the proposed multi-output SER over a range of benchmark schemes for real-time identification of multi-output nonlinear and nonstationary processes, in terms of both online identification accuracy and computational complexity.
2162-237X
1-14
Liu, Tong
2f1ae0c9-0c4a-4f2a-afcb-0c6c89504774
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Liang, Shan
8401b7cb-d5f6-4cfe-a44e-e2837d12d5a9
Gan, Shaojun
21ef0aca-936e-4e4a-8fad-a7380f67722d
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Liu, Tong
2f1ae0c9-0c4a-4f2a-afcb-0c6c89504774
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Liang, Shan
8401b7cb-d5f6-4cfe-a44e-e2837d12d5a9
Gan, Shaojun
21ef0aca-936e-4e4a-8fad-a7380f67722d
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Liu, Tong, Chen, Sheng, Liang, Shan, Gan, Shaojun and Harris, Christopher (2020) Multi-output selective ensemble identification of nonlinear and nonstationary industrial processes. IEEE Transactions on Neural Networks and Learning Systems, 1-14. (In Press)

Record type: Article

Abstract

A key characteristic of biological systems is the ability to update the memory by learning new knowledge and removing out-of-date knowledge so that intelligent decision can be made based on the relevant knowledge acquired in the memory. Inspired by this fundamental biological principle, this paper proposes a multi-output selective ensemble regression (SER) for online identification of multi-output nonlinear time-varying industrial processes. Specifically, an adaptive local learning approach is developed to automatically identify and encode newly emerging process state by fitting a local multi-output linear model based on the multi-output hypothesis testing. This growing strategy ensures a highly diverse and independent local model set. The online modeling is constructed as a multi-output SER predictor by optimizing the combining weights of the selected local multi-output models based on a probability metric. An effective pruning strategy is also developed to remove the unwanted out-of-date local multi-output linear models in order to achieve low online computational complexity without scarifying the prediction accuracy. A simulated two-output process and two real-world identification problems are used to demonstrate the effectiveness of the proposed multi-output SER over a range of benchmark schemes for real-time identification of multi-output nonlinear and nonstationary processes, in terms of both online identification accuracy and computational complexity.

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TNNLS-2020-1-13384 - Accepted Manuscript
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Accepted/In Press date: 26 September 2020

Identifiers

Local EPrints ID: 444298
URI: http://eprints.soton.ac.uk/id/eprint/444298
ISSN: 2162-237X
PURE UUID: 41aa745f-108b-4229-92a7-a7add8b57493

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Date deposited: 09 Oct 2020 16:35
Last modified: 26 Nov 2020 05:01

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Contributors

Author: Tong Liu
Author: Sheng Chen
Author: Shan Liang
Author: Shaojun Gan
Author: Christopher Harris

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