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

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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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Bibliographic Details
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/12047/1/25%20Haslinda%20Zabiri.pdf

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