Modeling of SRM Based on XS-LSSVR Optimized by GDS

Considering nonlinear magnetization characteristic of switched reluctance motor (SRM), this paper describes a nonlinear model of SRM based on an improved least square support vector machine regression (LSSVR) algorithm optimized by grid-diamond searching (GDS) method. The experimental results show that the GDS method can choose proper parameters of LSSVR while providing better simulation result. Modeling with the optimization parameter, the forecasted data of the model are compared with the experimental data on a four-phase, 8/6 SRM. It is shown that XS-LSSVR optimized by GDS is effectiveness method and performs better forecast accuracy and successful modeling of SRM.