Parallel based support vector regression for empirical modeling of nonlinear chemical process systems

In this paper, a support vector regression (SVR) using radial basis function (RBF) kernel is proposed using an integrated parallel linear-and-nonlinear model framework for empirical modeling of nonlinear chemical process systems. Utilizing linear orthonormal basis filters (OBF) model to represent th...

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Main Authors:	Haslinda Zabiri, Ramasamy Marappagounder, Nasser M. Ramli
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2018
Online Access:	http://journalarticle.ukm.my/12047/ http://journalarticle.ukm.my/12047/ http://journalarticle.ukm.my/12047/1/25%20Haslinda%20Zabiri.pdf

id	ukm-12047
recordtype	eprints
spelling	ukm-120472018-09-09T23:41:16Z http://journalarticle.ukm.my/12047/ Parallel based support vector regression for empirical modeling of nonlinear chemical process systems Haslinda Zabiri, Ramasamy Marappagounder, Nasser M. Ramli, In this paper, a support vector regression (SVR) using radial basis function (RBF) kernel is proposed using an integrated parallel linear-and-nonlinear model framework for empirical modeling of nonlinear chemical process systems. Utilizing linear orthonormal basis filters (OBF) model to represent the linear structure, the developed empirical parallel model is tested for its performance under open-loop conditions in a nonlinear continuous stirred-tank reactor simulation case study as well as a highly nonlinear cascaded tank benchmark system. A comparative study between SVR and the parallel OBF-SVR models is then investigated. The results showed that the proposed parallel OBF-SVR model retained the same modelling efficiency as that of the SVR, whilst enhancing the generalization properties to out-of-sample data. Penerbit Universiti Kebangsaan Malaysia 2018-03 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/12047/1/25%20Haslinda%20Zabiri.pdf Haslinda Zabiri, and Ramasamy Marappagounder, and Nasser M. Ramli, (2018) Parallel based support vector regression for empirical modeling of nonlinear chemical process systems. Sains Malaysiana, 47 (3). pp. 635-643. ISSN 0126-6039 http://www.ukm.my/jsm/malay_journals/jilid47bil3_2018/KandunganJilid47Bil3_2018.html
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	In this paper, a support vector regression (SVR) using radial basis function (RBF) kernel is proposed using an integrated parallel linear-and-nonlinear model framework for empirical modeling of nonlinear chemical process systems. Utilizing linear orthonormal basis filters (OBF) model to represent the linear structure, the developed empirical parallel model is tested for its performance under open-loop conditions in a nonlinear continuous stirred-tank reactor simulation case study as well as a highly nonlinear cascaded tank benchmark system. A comparative study between SVR and the parallel OBF-SVR models is then investigated. The results showed that the proposed parallel OBF-SVR model retained the same modelling efficiency as that of the SVR, whilst enhancing the generalization properties to out-of-sample data.
format	Article
author	Haslinda Zabiri, Ramasamy Marappagounder, Nasser M. Ramli,
spellingShingle	Haslinda Zabiri, Ramasamy Marappagounder, Nasser M. Ramli, Parallel based support vector regression for empirical modeling of nonlinear chemical process systems
author_facet	Haslinda Zabiri, Ramasamy Marappagounder, Nasser M. Ramli,
author_sort	Haslinda Zabiri,
title	Parallel based support vector regression for empirical modeling of nonlinear chemical process systems
title_short	Parallel based support vector regression for empirical modeling of nonlinear chemical process systems
title_full	Parallel based support vector regression for empirical modeling of nonlinear chemical process systems
title_fullStr	Parallel based support vector regression for empirical modeling of nonlinear chemical process systems
title_full_unstemmed	Parallel based support vector regression for empirical modeling of nonlinear chemical process systems
title_sort	parallel based support vector regression for empirical modeling of nonlinear chemical process systems
publisher	Penerbit Universiti Kebangsaan Malaysia
publishDate	2018
url	http://journalarticle.ukm.my/12047/ http://journalarticle.ukm.my/12047/ http://journalarticle.ukm.my/12047/1/25%20Haslinda%20Zabiri.pdf
first_indexed	2023-09-18T20:01:44Z
last_indexed	2023-09-18T20:01:44Z
_version_	1777406894077378560

Parallel based support vector regression for empirical modeling of nonlinear chemical process systems

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