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: | , , |
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| Format: | Article |
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Penerbit Universiti Kebangsaan Malaysia
2018
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| 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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| Summary: | 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. |
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