基于粒子群优化的SVM多分类的电动车价格预测研究

Study on Electric Vehicle Price Prediction Based on PSO-SVM Multi-classification Method

随着新能源汽车的推广,电动汽车逐渐进入千家万户,而影响电动汽车价格的因素较多。文中对影响电动汽车价格的20个属性进行主成分分析研究,先用Pearson相关系数法和PCA算法对数据进行预处理,获得比较重要的样本属性,然后对研究后的新数据进行多分类有监督学习。在支持向量机模型的基础上,用粒子群算法对支持向量机(Support Vector Machine,SVM)模型的参数进行优化选择,实现了对电动汽车的多分类研究,实验表明所建立的模型对电动汽车的多分类效果明显。

With the promotion of new energy vehicles,electric vehicles have gradually entered thousands of households.There are many factors that affect the price of electric vehicles.Twenty attributes that affect the price of electric vehicles are studied by principle component analysis.First of all,the data are preprocessed by Pearson correlation coefficient method and PCA algorithm to obtain more essential sample attributes.Then,the new data are studied by multi-classification supervised learning.Based on the SVM model,the particle swarm optimization algorithm is used to optimize the parameters of the support vector machine model,and the multi-classification research of electric vehicle is realized successfully.The experimental results show that the multi-classification SVM model has significant effect.