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Fast tunable gradient RBF networks for online modeling of nonlinear and nonstationary dynamic processes

Fast tunable gradient RBF networks for online modeling of nonlinear and nonstationary dynamic processes
Fast tunable gradient RBF networks for online modeling of nonlinear and nonstationary dynamic processes
Since most real-world processes exhibit both nonlinear and time-varying characteristics, there exists a need for accurate and efficient models that can adapt in nonstationary environments. Also for adaptive control purpose, it is vital that an adaptive model has a fixed small model size. In this paper, we propose an adaptive tunable gradient radial basis function (GRBF) network for online modeling of nonlinear dynamic processes, which meets these practical requirements. Specifically, a compact GRBF model is constructed by the orthogonal least squares algorithm in training, which is capable of modeling variations of local mean and trend in the data well. During online operation, the adaptive GRBF model tacks the time-varying process's dynamics by replacing a worst performing node with a new node which encodes the current new data. By exploiting the local predictor property of the GRBF node, the new node optimization can be done extremely efficiently. The proposed approach combining the advantages of both the GRBF network structure and fast tunable node mechanism is capable of tracking the time-varying nonlinear dynamics accurately and effectively. Extensive simulation results demonstrate that the proposed fast tunable GRBF network significantly outperforms the existing state-of-the-art methods, in terms of both adaptive modeling accuracy and online computational complexity.
0959-1524
53-65
Liu, Tong
64100bfa-0663-4b5a-8f9e-6beb0d3f2774
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Liang, Shan
cb5a612a-102d-44c0-b90f-9099b7258f7a
Du, Dajun
51bf8c9a-76bb-4901-8c89-65ce7034b6bf
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Liu, Tong
64100bfa-0663-4b5a-8f9e-6beb0d3f2774
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Liang, Shan
cb5a612a-102d-44c0-b90f-9099b7258f7a
Du, Dajun
51bf8c9a-76bb-4901-8c89-65ce7034b6bf
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Liu, Tong, Chen, Sheng, Liang, Shan, Du, Dajun and Harris, Christopher (2020) Fast tunable gradient RBF networks for online modeling of nonlinear and nonstationary dynamic processes. Journal of Process Control, 93, 53-65. (doi:10.1016/j.jprocont.2020.07.009).

Record type: Article

Abstract

Since most real-world processes exhibit both nonlinear and time-varying characteristics, there exists a need for accurate and efficient models that can adapt in nonstationary environments. Also for adaptive control purpose, it is vital that an adaptive model has a fixed small model size. In this paper, we propose an adaptive tunable gradient radial basis function (GRBF) network for online modeling of nonlinear dynamic processes, which meets these practical requirements. Specifically, a compact GRBF model is constructed by the orthogonal least squares algorithm in training, which is capable of modeling variations of local mean and trend in the data well. During online operation, the adaptive GRBF model tacks the time-varying process's dynamics by replacing a worst performing node with a new node which encodes the current new data. By exploiting the local predictor property of the GRBF node, the new node optimization can be done extremely efficiently. The proposed approach combining the advantages of both the GRBF network structure and fast tunable node mechanism is capable of tracking the time-varying nonlinear dynamics accurately and effectively. Extensive simulation results demonstrate that the proposed fast tunable GRBF network significantly outperforms the existing state-of-the-art methods, in terms of both adaptive modeling accuracy and online computational complexity.

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TGRBF_JPC_re1 - Accepted Manuscript
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More information

Accepted/In Press date: 30 July 2020
e-pub ahead of print date: 12 August 2020
Published date: September 2020

Identifiers

Local EPrints ID: 443130
URI: http://eprints.soton.ac.uk/id/eprint/443130
ISSN: 0959-1524
PURE UUID: 7b8d52be-f662-4997-acad-c90167cf4ecc

Catalogue record

Date deposited: 11 Aug 2020 16:34
Last modified: 06 Oct 2020 22:06

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