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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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Main Authors:	Ahmad Yasmin, Nur Sakinah, Abdul Wahab, Norhaliza, Danapalasingam, Kumerasan A.
Format:	Conference or Workshop Item
Published:	2022
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/98598/ http://dx.doi.org/10.1007/978-981-19-3923-5_46
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id	my.utm.98598
record_format	eprints
spelling	my.utm.985982023-01-21T01:15:52Z http://eprints.utm.my/id/eprint/98598/ Speed up grid-search for kernels selection of support vector regression Ahmad Yasmin, Nur Sakinah Abdul Wahab, Norhaliza Danapalasingam, Kumerasan A. 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. 2022 Conference or Workshop Item PeerReviewed Ahmad Yasmin, Nur Sakinah and Abdul Wahab, Norhaliza and Danapalasingam, Kumerasan A. (2022) Speed up grid-search for kernels selection of support vector regression. In: 3rd International Conference on Control, Instrumentation and Mechatronics Engineering, CIM 2022, 2 - 3 March 2022, Virtual, Online. http://dx.doi.org/10.1007/978-981-19-3923-5_46
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Ahmad Yasmin, Nur Sakinah Abdul Wahab, Norhaliza Danapalasingam, Kumerasan A. Speed up grid-search for kernels selection of support vector regression
description	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.
format	Conference or Workshop Item
author	Ahmad Yasmin, Nur Sakinah Abdul Wahab, Norhaliza Danapalasingam, Kumerasan A.
author_facet	Ahmad Yasmin, Nur Sakinah Abdul Wahab, Norhaliza Danapalasingam, Kumerasan A.
author_sort	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
publishDate	2022
url	http://eprints.utm.my/id/eprint/98598/ http://dx.doi.org/10.1007/978-981-19-3923-5_46
_version_	1755872331309252608
score	13.211869

Speed up grid-search for kernels selection of support vector regression

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