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 a highly nonlinear cascaded tank benchmark system. A comparative study between SVR and the parallel
OBF-SVR models is then investigated. The results showed that the proposed parallel OBF-SVR model retained the same
modelling efficiency as that of the SVR, whilst enhancing the generalization properties to out-of-sample data.