基于模糊神经网络的铁路车辆热轴等级判别模型  

Hotbox level detection of railway vehicle using fuzzy neural networks

作　　者：崔转玲[1] 李国宁[1] 林森[2] 

机构地区：[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

出　　处：《计算机应用》2013年第9期2566-2569,共4页journal of Computer Applications

摘　　要：针对铁路红外线轴温监测系统中热轴判别准确率低、判别算法简单、判别参数多且不易修改等问题,提出了基于模糊神经网络的热轴判别模型。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%。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 he 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%.

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 

分 类 号：U270.7[机械工程—车辆工程] TP183[交通运输工程—载运工具运用工程]