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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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書誌詳細
主要な著者:	Zabiri, H., Marappagounder, R., Ramli, N.M.
フォーマット:	論文
出版事項:	Penerbit Universiti Kebangsaan Malaysia 2018
オンライン･アクセス:	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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