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: | 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/ http://journalarticle.ukm.my/12047/1/25%20Haslinda%20Zabiri.pdf |
Similar Items
-
Support vector regression based friction modeling and compensation in motion control system
by: Tijani, Ismaila, et al.
Published: (2012) -
Vector regression estimation and linear transformation
by: Daoud, Jamal Ibrahim, et al.
Published: (2008) -
Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain
by: Fradinata, E., et al.
Published: (2019) -
Parallelization of logic regression analysis on SNP-SNP
interactions of a Crohn’s disease dataset model
by: Unitsa Sangket,, et al.
Published: (2017) -
Heteroscedastic nonlinear regression by using Tanh Psi function
by: Habshah Mid,
Published: (2000)