A modeling method of SRM based on RBF neural networks

Abstract

This paper presents a modeling method of Switched Reluctance Motor (SRM) based on the Radial Basis Function (RBF) Neural Networks. By analysing measuring data and nonlinear characteristics of SRM, the modeling of SRM is designed with Gaussion Function. The simulated results show that the proposed model has better capability of generalization and correctly represents the characteristics of SRM compared with traditional method of local linearization or BP Neural Networks, which is more significative to real-time control for SRM.