基于模糊综合评价和BP神经网络的车辆危险状态辨识  

Identification of vehicle risk status based on fuzzy comprehensive evaluation and BP neural network

作　　者：李世武[1] 田晶晶[1] 沙学锋[2] 孙文财[1] 王琳虹[1] 

机构地区：[1]吉林大学交通学院,长春130022 [2]中国人民解放军装甲兵技术学院机械工程系,长春130033

出　　处：《吉林大学学报（工学版）》2011年第6期1609-1613,共5页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(50978116);吉林大学杰出青年基金项目(201005011)

摘　　要：考虑驾驶员、车辆、环境和管理因素对车辆安全状态的影响,建立了车辆安全状态评价体系。采用模糊层次分析法确定了各评价指标的权重,利用岭型函数建立了评价指标对评价等级的隶属度函数,在Matlab神经网络工具箱中构建了车辆安全状态神经网络评价模型。采用预设故障的试验方法,利用车辆监测预警系统采集道路试验过程中车辆的运行状态参数。通过模糊综合评价得出神经网络所需的训练样本,运用训练好的BP神经网络对道路试验中车辆安全状态进行评价。道路试验结果表明:神经网络评价较模糊综合评价灵敏度更高、更准确,为车辆危险状态辨识提供了一种新方法。The weight vector of the evaluation indexes was determined by the fuzzy hierarchy analysis,the membership functions of the evaluation indexes to the evaluation class were established using the ridge function,and a BP neural network model for evaluation of the vehicle safety status was built using the neural network tool box in Matlab software.The experiments were performed using the preset fault test method,the operation parameters of the vehicle under the road test process were acquired by the vehicle monitoring and early-warning system.The training samples for the neural network were obtained by the fuzzy comprehensive evaluation,and the trained BP neural network was used to evaluate the vehicle safety status in the road test.The road test results showed that the neural network evaluation is more sensitive and more accurate than the fuzzy comprehensive evaluation,providing a new method for identification of the vehicle risk status.

关 键 词：交通运输工程 车辆安全状态 模糊综合评价 BP神经网络 

分 类 号：U491.8[交通运输工程—交通运输规划与管理]