CAJ | 학술논문

为了有效利用多种检测数据评判轨道单元的状态，提出利用 LVQ (学习矢量量化)神经网络建立轨道单元特征参数与轨道单元分级的关联模型，通过对 TQI (轨道质量指数)、轨道几何、加速度、晃车仪、添乘仪、人体感觉的超限扣分加权得到轨道单元的量化评分指标，并利用层次分析法确定各特征参数的权系数。根据大量实测数据建立随机样本，利用聚类方法确定轨道单元状态的分级。以轨道单元的量化评分指标作为输入，以聚类得到的表征轨道单元分级的矢量量化数据作为输出，利用误差反向传播方法训练 LVQ 神经网络模型。利用新的评判方法对某线路的轨道单元状态进行评判，结果表明该方法可行、有效，为轨道单元状态综合评判提供了一条新途径。
위료유효이용다충검측수거평판궤도단원적상태，제출이용 LVQ (학습시량양화)신경망락건립궤도단원특정삼수여궤도단원분급적관련모형，통과대 TQI (궤도질량지수)、궤도궤하、가속도、황차의、첨승의、인체감각적초한구분가권득도궤도단원적양화평분지표，병이용층차분석법학정각특정삼수적권계수。근거대량실측수거건립수궤양본，이용취류방법학정궤도단원상태적분급。이궤도단원적양화평분지표작위수입，이취류득도적표정궤도단원분급적시량양화수거작위수출，이용오차반향전파방법훈련 LVQ 신경망락모형。이용신적평판방법대모선로적궤도단원상태진행평판，결과표명해방법가행、유효，위궤도단원상태종합평판제공료일조신도경。
The paper proposes to build the correlation model for the track unit under discussion,intending to study the relation between its characteristic parameters and its classification,based on Learning Vector Quantization ( LVQ) neural network,so as to evaluate the state of the tract unit through a large range of data. In this case,analytic hierarchy process can be used to determine the weighted coefficient for each characteristic parameter,therefore quantified assessment index can be reached as track quality index ( TQI) ,track geometry,acceleration,vibration-acceleration inspection devices and passengers's general feeling were quantified and deducted by its weighted sum in excessive cases. Random samples were built based on a considerable amount of data drawn from measurement and the classification of the track unit was determined with the introduction of cluster analysis. The error back propagation was applied to further improve the LVQ neural network model with the quantified assessment index as input and the vector quantification data as output. The papar evaluated the track unit state with the help of the new evaluating method,which turned out to be plausible and effective,therefore provide new approach for the integrated assessment of track unit.