A Multi-mode Prediction Method for Elevator Traffic Flow Based on Classification Off-line

作     者：Zong Qun 1,Wang Weijia 1,Shang Anna 2 1.School of Electrical Engineering and Automation,Tianjin University,Tianjin 300072,P.R.China 2.Department of Electrical Engineering,Shaanxi University of Technology,Hanzhong Shaanxi 723001,P.R.China 

会议名称：《第二十七届中国控制会议》

会议日期：2008年

学科分类：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] 082303[工学-交通运输规划与管理] 0835[工学-软件工程] 0813[工学-建筑学] 0814[工学-土木工程] 0811[工学-控制科学与工程] 082302[工学-交通信息工程及控制] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：supported by National Natural Science Foundation of China(NSFC)under Grant 60574055 supported by Specialized Research Fund for Doctoral Program of Higher Education(SRFDP)under Grant 20050056037 

关 键 词：Elevator Traffic Flow Multi-mode Prediction Artificial Immune C-Means Clustering Algorithm Gaussian Mixture Model EM Algorithm 

摘      要：In order to achieve the on-line traffic flow prediction,a novel multi-mode prediction method based on mode classification off-line is ***,the elevator traffic flow is classified off-line into patterns by the two-stage Clustering Algorithm,Artificial Immune C-Means Clustering Algorithm（AI C-Means CA） .Then Gaussian Mixture Model（GMM） is used to model the multi-mode elevator traffic *** the EM algorithm is utilized to estimate the parameters of GMM to predict elevator traffic flow on ***,the effectivity of this prediction method is validated by comparing simulation results with other prediction methods.