基于BP神经网络的汽车尾气检测系统设计

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英文篇名：Design of automobile exhaust detection system based on BP neural network 作者：周唤雄 英文作者：Zhou Huanxiong;Gansu Vocational and Technical College of Communications; 关键词：BP神经网络 ; 汽车尾气 ; 检测系统 ; C#语言 英文关键词：BP neural network;;automobile exhaust;;detection system;;C# language 中文刊名：汽车实用技术 英文刊名：Automobile Applied Technology 机构：甘肃交通职业技术学院; 出版日期：2019-09-29 出版单位：汽车实用技术 年：2019 期：18 语种：中文; 页：115-116+134 页数：3 CN：61-1394/TH ISSN：1671-7988 分类号：X831;TP183

摘要

汽车使用量的快速增长,在很大程度上解决了人们的出行问题,但同时也向环境保护提出了更大的挑战。为检测车辆排放的尾气中污染物的种类与数量,设计汽车尾气检测系统,介绍了系统中软件与硬件组成,基于C#语言开发上位机软件对采集数据进行处理与显示,并在检测系统中加入基于BP神经网络的预测模型。通过实验结果分析可知,该测量系统具有较强的检测能力和较高的检测精度,能够对5种气体进行准确测量,此外,将BP神经网络模型应用在该尾气检测系统中,使得预测系统具有更高的预测精度,收敛速度大幅度增加,能够较好地适应汽车尾气预测系统,解决实际预测难题。
        The rapid growth of car use has largely solved the problem of people's travel, but it also poses a greater challenge to environmental protection. In order to detect the types and quantities of pollutants in vehicle exhaust, the automobile exhaust detection system is designed, and the software and hardware components of the system are introduced. The upper computer software based on C# language is developed to process and display the collected data, and a prediction model based on BP neural network is added to the detection system. By analysing the experimental result shows that the measuring system has strong ability of detection and high detection accuracy, can carry on the accurate measurement of five kinds of gas, in addition, the BP neural network model used in the tail gas detection system, made the forecast system has higher prediction accuracy, convergence speed increase, can better adapt to the automobile exhaust prediction system, to solve practical prediction problem.

引文

[1]李钢,谷威,刘进,等.在用汽车尾气遥测法检测及排放限值的研究[J].环境科学与技术,2016(S2):378-383.
    [2]张坤,陈锋.非分光红外车辆尾气传感器系统设计[J].传感器与微系统,2016,35(2):130-132.
    [3]徐亚丹,工俊,赵国军.检测掺假牛奶的电子鼻传感器阵列的优化[J].传感技术学报,2006(4):957-962.
    [4]刘萍,简家文,陈志芸.基于集成神经网络的汽车尾气检测系统设计[J].环境工程学报,2016(4):1883-1887.
    [5]张雪.城市轨道交通社会效益研究[D].哈尔滨:哈尔滨工业大学,2011.
    [6]马超云.城市轨道交通换乘站列车时刻表的协调和优化[D].北京:北京交通大学,2010.