ACO-BP神经网络在电梯交通流预测中的应用  

Application of ACO-BP neural network in elevator traffic flow prediction

作　　者：万健如[1] 贾祖新 智渊[2] 

机构地区：[1]天津大学电气与自动化工程学院,天津300072 [2]重庆能源职业学院电梯学院,重庆402260

出　　处：《传感器与微系统》2015年第11期153-156,160,共5页Transducer and Microsystem Technologies

基　　金：国家质检总局公益性科研专项经费项目(201310153)

摘　　要：电梯交通流预测为电梯配置与群控调度提供必要的乘客流数据信息。针对基于BP神经网络的电梯交通流预测模型在网络训练过程中表现出的对初值敏感、易陷入极小值等问题,提出利用全局寻优的蚁群优化(ACO)算法优化BP神经网络。同时,利用精英蚂蚁和排序策略对基本ACO算法进行改进。采集天津某办公大厦实际交通流数据进行实例分析,分别对基于传统的BP神经网络和蚁群优化的BP(ACO-BP)神经网络的电梯交通流预测模型进行仿真验证。结果表明:ACO-BP神经网络的预测效果远优于传统的BP神经网络,适用于电梯交通流预测系统。Elevator traffic flow prediction can provide essential traffic data information for elevator configuration and group control scheduling. Considering that the established elevator traffic flow prediction model based on BP neural network is sensitive to initial value and easy to get trapped in some local minimum value in process of neural network training,ant colony optimization（ ACO） algorithm with global optimization capability is proposed to optimize BP neural network. Besides,elitist ants and rank-based strategy are used to improve the basic ACO algorithm. The real elevator traffic flow data of an office building in Tianjin are collected to analyze and simulate these models. Simulation results show that the prediction effect of ACO-BP neural network is far superior to traditional BP neural network and ACO-BP is suitable for the elevator traffic flow prediction system.

关 键 词：电梯 交通流预测 BP神经网络 蚁群优化 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]