LS-SVM在电梯交通流预测中的应用  

The Application of LS-SVM to the Prediction of the Elevator Transportation Flow

作　　者：计丽霞[1] 付晓刚[1] 

机构地区：[1]上海电机学院电气学院,上海200240

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)

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.

关 键 词：电梯交通流 预测 最小二乘支持向量机 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]