A Support Vector Regression Nonlinear Model for SiC MESFET
Support vector machine (SVM) regression approach is introduced in this paper for table-based nonlinear modeling of field effect transistors (FET). Support vector machine, which based on statistical learning theory and structural risk minimization (SRM) principle, is provided with good generalization ability. For the purpose of demonstration, a table-based SVM regression model is established using a set of training data and testing data produced by an available empirical nonlinear model of SiC MESFET. Experimental results are also given out to validate its good ability in predicting electrical performance.