Speed up grid-search for Kernels selection of support vector regression
The aerobic granular sludge (AGS) is a one of the promising technologies for wastewater treatment. In this paper, several modelling strategies are developed to predict the behaviour of AGS. The modelling approaches are cautiously chosen to address the complex dynamic of AGS due internal interactions...
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2022
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my.utm.1008742023-05-18T03:50:30Z http://eprints.utm.my/id/eprint/100874/ Speed up grid-search for Kernels selection of support vector regression Ahmad Yasmin, Nur Sakinah Abdul Wahab, Norhaliza A. Danapalasingam, Kumerasan TK Electrical engineering. Electronics Nuclear engineering The aerobic granular sludge (AGS) is a one of the promising technologies for wastewater treatment. In this paper, several modelling strategies are developed to predict the behaviour of AGS. The modelling approaches are cautiously chosen to address the complex dynamic of AGS due internal interactions between the sludge characteristics and variables. Since only a small dataset is available, the support vector regression (SVR) method is employed. Instead of using the time-consuming and trial-and-error or grid search methods to determine the pair of kernels, the particle swarm optimization (PSO) and genetic algorithm (GA) techniques are proposed. Using a dataset generated from an AGS process in sequential batch reactor at a working temperature 30 ˚C, the SVR-PSO, SVR-GA and SVR-Grid Search predict models are developed and compared. The results show that the proposed SVR-PSO and SVR-GA models improve the prediction accuracy of chemical oxygen demand (COD) by 10% as compared to the conventional SVR-Grid Search model. The computational time also was reduced up to 86% and 79% respectively. Springer Science and Business Media Deutschland GmbH 2022 Book Section PeerReviewed Ahmad Yasmin, Nur Sakinah and Abdul Wahab, Norhaliza and A. Danapalasingam, Kumerasan (2022) Speed up grid-search for Kernels selection of support vector regression. In: Control, Instrumentation and Mechatronics: Theory and Practice. Lecture Notes in Electrical Engineering, 921 (NA). Springer Science and Business Media Deutschland GmbH, Singapore, pp. 532-544. ISBN 978-981193922-8 http://dx.doi.org/10.1007/978-981-19-3923-5_46 DOI:10.1007/978-981-19-3923-5_46 |
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TK Electrical engineering. Electronics Nuclear engineering Ahmad Yasmin, Nur Sakinah Abdul Wahab, Norhaliza A. Danapalasingam, Kumerasan Speed up grid-search for Kernels selection of support vector regression |
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The aerobic granular sludge (AGS) is a one of the promising technologies for wastewater treatment. In this paper, several modelling strategies are developed to predict the behaviour of AGS. The modelling approaches are cautiously chosen to address the complex dynamic of AGS due internal interactions between the sludge characteristics and variables. Since only a small dataset is available, the support vector regression (SVR) method is employed. Instead of using the time-consuming and trial-and-error or grid search methods to determine the pair of kernels, the particle swarm optimization (PSO) and genetic algorithm (GA) techniques are proposed. Using a dataset generated from an AGS process in sequential batch reactor at a working temperature 30 ˚C, the SVR-PSO, SVR-GA and SVR-Grid Search predict models are developed and compared. The results show that the proposed SVR-PSO and SVR-GA models improve the prediction accuracy of chemical oxygen demand (COD) by 10% as compared to the conventional SVR-Grid Search model. The computational time also was reduced up to 86% and 79% respectively. |
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Book Section |
author |
Ahmad Yasmin, Nur Sakinah Abdul Wahab, Norhaliza A. Danapalasingam, Kumerasan |
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Ahmad Yasmin, Nur Sakinah Abdul Wahab, Norhaliza A. Danapalasingam, Kumerasan |
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Ahmad Yasmin, Nur Sakinah |
title |
Speed up grid-search for Kernels selection of support vector regression |
title_short |
Speed up grid-search for Kernels selection of support vector regression |
title_full |
Speed up grid-search for Kernels selection of support vector regression |
title_fullStr |
Speed up grid-search for Kernels selection of support vector regression |
title_full_unstemmed |
Speed up grid-search for Kernels selection of support vector regression |
title_sort |
speed up grid-search for kernels selection of support vector regression |
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Springer Science and Business Media Deutschland GmbH |
publishDate |
2022 |
url |
http://eprints.utm.my/id/eprint/100874/ http://dx.doi.org/10.1007/978-981-19-3923-5_46 |
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