摘要：磁链特性是开关磁阻电机系统建模的重要基础，文中探讨了利用极限学习机处理磁化曲线簇，建立电机模型的方法。在详细分析电机非线性磁链特性的基础上，运用极限学习机的非线性回归理论，通过对开关磁阻电机进行有限元分析得到样本数据集进行训练学习，建立了电机的非参数模型，与传统神经网络、支持向量机相比，该电机模型具有更高的回归精度与更快的回归速度。仿真实验表明，该模型比较准确地反映了电机的磁链特性。

关键词：开关磁阻电机；极限学习机；非线性磁特性；建模
Abstract: Flux-linkage characteristics were vital for modeling of switched reluctance motors, and a novel approach was proposed in this paper that modeling of flux linkage in switched reluctance motors was achieved by utilizing extreme learning machine. With flux-linkage characteristics were analyzed in details, sample data were obtained by finite element analysis for non-parametric modeling using non-linear regression theory of extreme learning machine. Compared with traditional artificial neural network (such as BP neural network, RBF neural network, etc.) and support vector machine, extreme learning machine demonstrated a higher precision and a faster speed of regression. Finally, it was proved that the trained non-parametric model indicated flux-linkage characteristics accurately.

Key words: switched reluctance motors；extreme learning machine；flux-linkage characteristics；modeling