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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 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 adoi:10.17576/jsm-2018-4703-25 fatcat:5xtplhbferhzrdfjlqj2czaorm