Prediction of elevator traffic flow based on SVM and phase space reconstruction

作     者：唐海燕 齐维贵 丁宝 

作者机构：School of Electrical Engineering and AutomationHarbin Institute of Technology 

出 版 物：《Journal of Harbin Institute of Technology(New Series)》 (哈尔滨工业大学学报（英文版）)

年 卷 期：2011年第18卷第3期

页      面：111-114页

核心收录：

学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Sponsored by the National Eleventh Five year Plan Key Project of Ministry of Science and Technology of China (Grant No. 2006BAJ03A05-05) 

主　　题：support vector machine phase space reconstruction prediction of elevator traffic flow RBF neural network 

摘      要：To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ***,the phase space reconstruction for elevator traffic flow time series (ETFTS) is ***,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of *** prediction model of ETFTS based on SVM is ***,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some ***,it is compared with RBF neural network *** results show that the trend of factual traffic flow is better followed by predictive traffic *** algorithm has much better prediction *** fitting and prediction of ETF with better effect are realized.