计算机应用 ›› 2013, Vol. 33 ›› Issue (09): 2566-2569.DOI: 10.11772/j.issn.1001-9081.2013.09.2566

• 人工智能 • 上一篇    下一篇

基于模糊神经网络的铁路车辆热轴等级判别模型

崔转玲1,李国宁1,林森2   

  1. 1. 兰州交通大学 自动化与电气工程学院,兰州 730070;
    2. 兰州交通大学 机电工程学院,兰州 730070
  • 收稿日期:2013-04-01 修回日期:2013-05-12 出版日期:2013-09-01 发布日期:2013-10-18
  • 通讯作者: 崔转玲
  • 作者简介:崔转玲(1988-),女,陕西咸阳人,硕士研究生,主要研究方向:智能信息处理;
    李国宁(1959-),男,宁夏中宁人,副教授,主要研究方向:交通信息工程及控制;
    林森(1988-),男,陕西渭南人,硕士研究生,主要研究方向:铁路车辆装备状态监测及故障诊断。

Hotbox level detection of railway vehicle using fuzzy neural networks

CUI Zhuanling1,LI Guoning1,LIN Sen2   

  1. 1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou Gansu 730070, China;
    2. School of Mechatronic Engineering, Lanzhou Jiaotong University, Lanzhou Gansu 730070, China;
  • Received:2013-04-01 Revised:2013-05-12 Online:2013-10-18 Published:2013-09-01
  • Contact: CUI Zhuanling

摘要: 判别模型。该模型选定了温升、列温升差、辆温升差3个特征作为输入量,4种热轴等级作为输出量,并利用125条模糊推理规则和学习算法对模糊神经网络进行训练,得到的模糊神经网络可作为专家系统对热轴进行判别。实例仿真结果表明:模糊神经网络热轴判别模型使得判别参数减少,判别科学化,且判别的一致率达到95%。

关键词: 红外线轴温监测系统, 铁路车辆, 等级, 热轴判别, 模糊神经网络

Abstract: Concerning the low accuracy, simple algorithm and multiple parameters but difficulty in modification of hotbox detection of Infrared Train Hotbox Detecting System (THDS), a new hotbox detection model based on fuzzy neural networks was proposed. The model selected three variables as inputs, such as temperature difference, train temperature difference and vehicle temperature difference, and four hotbox grades as outputs. One hundred and twenty-five fuzzy rules and learning algorithm were used to train the fuzzy neural networks, which can be as expert system to detect hot axis. The practical simulation results show that the hotbox detection model using fuzzy neural networks can reduce the number of detecting parameters, and the discrete concordance rate reaches 95%.

Key words: Train Hotbox Detecting System (THDS), railway vehicle, grade, hotbox detection, fuzzy neural networks (FNN)

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