基于径向基函数神经网络的开关磁阻电机建模

Modeling of Switched Reluctance Motors Based on Radial Basis Function Neural Networks

基于径向基函数神经网络的局部逼近理论 ,利用高斯基函数 ,在分析测量数据和开关磁阻电机非线性磁特性的基础上 ,建立了开关磁阻电机的模型。通过与样机实测数据比较 ,验证了模型的有效性。与传统的局部线性化方法及BP神经网络比较 ,本文所建模型有更好的泛化能力和更快的速度 ,比较准确地反映了开关磁阻电机的磁特性 。

The paper analysis nonlinear characteristics of swi tc hed reluctance motors (SRM). The modeling of SRM based on radial basis function (RBF) neural networks is designed with Gaussian function. Experimental results v alidates the proposed model. The simulated results show that the proposed model has better capability of generalization and more quickly velocity, and represent more correctly the characteristics of SRM compared with traditional method of l ocal linearization or BP networks, which is critical to real-time control for S RM.