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: Zabiri, H., Marappagounder, R., Ramli, N.M.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.21730 /
Published: Penerbit Universiti Kebangsaan Malaysia 2018
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045654380&doi=10.17576%2fjsm-2018-4703-25&partnerID=40&md5=ab0ea71399a142639b56a8c597e3f7a6
http://eprints.utp.edu.my/21730/
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spelling utp-eprints.217302018-11-07T03:21:46Z Parallel based support vector regression for empirical modeling of nonlinear chemical process systems Zabiri, H. Marappagounder, R. Ramli, N.M. 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. © 2018 Penerbit Universiti Kebangsaan Malaysia. All Rights Reserved. Penerbit Universiti Kebangsaan Malaysia 2018 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045654380&doi=10.17576%2fjsm-2018-4703-25&partnerID=40&md5=ab0ea71399a142639b56a8c597e3f7a6 Zabiri, H. and Marappagounder, R. and Ramli, N.M. (2018) Parallel based support vector regression for empirical modeling of nonlinear chemical process systems. Sains Malaysiana, 47 (3). pp. 635-643. http://eprints.utp.edu.my/21730/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
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. © 2018 Penerbit Universiti Kebangsaan Malaysia. All Rights Reserved.
format Article
author Zabiri, H.
Marappagounder, R.
Ramli, N.M.
spellingShingle Zabiri, H.
Marappagounder, R.
Ramli, N.M.
Parallel based support vector regression for empirical modeling of nonlinear chemical process systems
author_sort Zabiri, H.
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 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045654380&doi=10.17576%2fjsm-2018-4703-25&partnerID=40&md5=ab0ea71399a142639b56a8c597e3f7a6
http://eprints.utp.edu.my/21730/
_version_ 1741196511174197248
score 11.62408

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