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...
| Main Authors: | Zabiri, H., Marappagounder, R., Ramli, N.M. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.20647 / |
| Published: |
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/20647/ |
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