摘要
支持向量机是一种基于统计学理论学习的新颖的机器学习方法,该方法已广泛应用于解决分类和回归问题。提出一种基于时间序列的最小二乘支持向量机算法应用于电梯交通流的预测方法。仿真结果表明了这种预测方法的有效性。
Least squares-support vector machines（LS-SVM） is a kind of novel machine learning method based on statistics theory study, which has been extensively applied to solve the problems of classification and regression. A prediction method of LS-SVM based on time series is presented and applied to the elevator traffic flow. The simulation experiment shows the effectiveness of this kind of prediction method.
出处
《上海电机学院学报》
2006年第3期62-64,共3页
Journal of Shanghai Dianji University
基金
上海电机学院科研项目(C1-0806-0503)
关键词
电梯交通流
预测
最小二乘支持向量机
elevator transportation series
prediction
least squares-support vector machines
