Elevator Group-Control Policy Based on Neural Network Optimized by Genetic Algorithm

作     者：沈虹 万健如 张志超 刘英培 李光叶 SHEN Hong1,WAS Jianru1, ZHANG Zhi chao2, LIU Yingpei1,LI Auangye1 (1. School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China;2. Baoding Power Supply Company, Baoding 071000, China)

作者机构：School of Electrical Engineering and Automation Tianjin University Baoding Power Supply Company 

出 版 物：《Transactions of Tianjin University》 (天津大学学报（英文版）)

年 卷 期：2009年第15卷第4期

页      面：245-248页

核心收录：

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] 0813[工学-建筑学] 0835[工学-软件工程] 0814[工学-土木工程] 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：Supported by National Natural Science Foundation of China (No60874077) Specialized Research Funds for Doctoral Program of Higher Education of China (No20060056054) Research Funds for Scientific Financing Projects of Quality Control Public Welfare Profession (No2007GYB172) 

主　　题：elevator group control genetic algorithm neural network hybrid algorithm 

摘      要：Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algorithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance.