基于灰色神经网络的轨道质量指数预测
首发时间:2009-10-13
摘要:轨道质量指数是目前我国铁路应用的一种主要的均值评价方法,能够真实、准确地反映出轨道不平顺的质量状态。通过轨检车实测TQI数据作为原始数据序列构造灰色神经网络模型(GNNM),该模型将灰色系统方法与神经网络方法通过有效途径结合起来,可对复杂的不确定性问题求解。对算例进行精度分析,得到满意的预测结果,表明该灰色神经网络模型预测精度高,是一种用于掌握轨道质量状态的新方法。
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A Prediction Method for Track Quality Index Based on Grey Neural Network Model
Abstract:Track Quality Index is currently one of the main mean evaluation applied in China’s railways; it can truly and accurately reflect the quality and state of the track. Based on the data detected by the track inspection car, which can be used as the original data, this paper constructed a Gray Neural Network Model. This model combines gray system method and neural network method through effective means, which can solve uncertain and complex problems. At last this paper takes analysis of accuracy on one example, and the forecasting results are satisfied. The results show that the Gray Neural Network Model is accurate in prediction and it is a new method easy to grasp the quality and state of the track.
Keywords: Track Quality Index Gray Neural Network Model Grey System Prediction
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No.3577449523912554****
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