摘要
针对开关磁阻电机的非线性磁链特性,用最小二乘支持向量机(least square support vector machine,LSSVM)与自适应遗传算法相结合的方法精确构建开关磁阻电动机的磁链模型。在最小二乘支持向量机通过采样数据训练模型的过程中,用自适应遗传算法评价拟合误差,优化LSSVM模型的超参数,进而优化开关磁阻电机的磁链模型。通过比较该模型的预测数据与实际测量数据,可以得出用自适应遗传算法优化的最小二乘支持向量机构建的开关磁阻电机模型是可行的,有较高的精度和较好的预测能力。
Considering the nonlinear flux-linkage characteristic of switched reluctance motor （SRM）, least square support vector machine （LSSVM） optimized by adaptive genetic algorithm （AGA） and implemented it for modeling nonlinear characteristic of SRM. When the LSSVM is trained with sufficient sample data, AGA is applied to optimize super parameters of LSSVM via minimizing fitting errors between forecasted data and measured data. With the trained LSSVM, the forecasted data of the model are compared with measured data, and error analyses are given to evaluate performances of the proposed model. The experimental results demonstrate that LSSVM optimized by AGA performs better forecast accuracy and successful modeling of SRM.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2009年第12期65-69,共5页
Proceedings of the CSEE
基金
国家自然科学基金项目(50575175)~~
关键词
开关磁阻电机
最小二乘支持向量机
自适应遗传算法
建模
优化
switched reluctance motor
least square support vector machine
adaptive genetic algorithm
modeling
optimization
