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Comparative study of support vector regression model under different kernel functions

Author Affiliations

  • 1Nigerian Institute of Medical Research, Lagos, Nigeria

Res. J. Mathematical & Statistical Sci., Volume 8, Issue (1), Pages 1-5, January,12 (2020)

Abstract

In spite of the fast growth in computer experiments, some computer models remain complex and often too time-consuming to be applied directly to mimic the experimentation conducted in the laboratory and predict the response of computer models using entirely a new set of data. To circumvent these problems and simplify the burden in using a simulator, a metamodel named as a support vector regression (SVR) model was used in this study as an emulator of a simple pendulum computer (SPC) model. The efficiencies of the SVR model under Gaussian Radial Basis (GRB) and B-Splines (BS) Kernel functions were examined. A SPC experiment developed using Orthogonal Array (OA)-based Latin Hypercube Design (LHD) was adopted to demonstrate the goodness of the metamodel. Further performance of SVR model at predicting the stoppage time of a pendulum bob was checked using the two kernel functions. Comparisons were also made on the performances of SVR model using R-square, relative average absolute error (RAAE) and relative maximum absolute error (RMAE), respectively. Estimated values for RAAE (0.0458) and RMAE (0.0500) provided by B-Splines functions are relatively smaller than the estimated values for RAAE (0.0604) and RMAE (0.2686) provided by the GRB function. Further results also showed that the SVR model with GRB kernel function has the estimated R2(0.9936) that is relatively smaller than the one provided by the B-Splines function (0.9977). This study concluded that the SVR model with B-Splines kernel function outperforms the one that uses Gaussian Radial Basis function for modelling and predicting the stoppage time of a pendulum bob at untried inputs. The model developments and analyses were performed using 9.0.0.341360 (R2016a) MATLAB software.

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