| 基于粒子群算法-最小二乘支持向量机算法的磁化曲线拟合 |
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| 引用本文： | 王娟,刘明光. 基于粒子群算法-最小二乘支持向量机算法的磁化曲线拟合[J]. 航空动力学报, 2017, 44(7): 26-29 |
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| 作者姓名： | 王娟 刘明光 |
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| 作者单位： | 北京交通大学 电气工程学院,北京100044,北京交通大学 电气工程学院,北京100044 |
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| 基金项目： | 中央高校基本科研业务费专项资金资助项目(2015JBM085) |
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| 摘 要： | 磁化曲线是强非线性函数,提高磁化曲线的拟合精度对含有铁磁材料的电气设备建模准确性至关重要。提出了一种基于粒子群算法-最小二乘支持向量机(PSOLSSVM)算法的磁化曲线拟合方法。该方法用粒子群优化算法解决了最小二乘支持向量机(LSSVM)参数的选择问题。仿真结果显示PSOLSSVM算法能获得最优的LSSVM参数,且采用PSOLSSVM算法拟合的磁化曲线与实际测量的磁化曲线基本无偏差,拟合精度较高。
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| 关 键 词： | 磁化曲线 最小二乘支持向量机 粒子群算法 曲线拟合 参数优化 |
Curve Fitting of Excitation Characteristics Based on Particle SwarmOptimizationLeast Squares Support Vector Machine Algorithm |
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| WANG Juan and LIU Mingguang. Curve Fitting of Excitation Characteristics Based on Particle SwarmOptimizationLeast Squares Support Vector Machine Algorithm[J]. Journal of Aerospace Power, 2017, 44(7): 26-29 |
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| Authors: | WANG Juan and LIU Mingguang |
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| Affiliation: | School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China and School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China |
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| Abstract: | Magnetization curve was strongly nonlinear function. It was important to improve the accuracy of the magnetization curve fitting for the model of electrical equipment containing ferromagnetic material. Therefore, a method of magnetization curve fitting based on PSOLSSVM algorithm was proposed. The method used particle swarm optimization algorithm to solve the LSSVM parameters selection problem. The simulation results showed that PSOLSSVM algorithm could obtain optimal LSSVM parameters and the magnetization curve used PSOLSSVM algorithm has high fitting accuracy. |
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| Keywords: | magnetization curve least squares support vector machine (LSSVM) particle swarm optimization (PSO) curve fitting parameter optimization |
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