将本文作者提出的灰色非线性模型(该内容发表于《铁道学报》2010年第2期)视为由一系列趋势项发展系数和随机项系数控制的时程函数,并将其系数作为系统分析中的辨识参数,表征系统随时间发展特征属性.在已知大机捣固作业效率或初始质量条件下,认为不同维修周期内轨道质量系统发展存在“相关性”,将已知维修周期内TQI时间序列挖掘出的辨识参数作为预测维修周期内TQI发展的特征参数,建立轨道质量生命周期预测模型.实例证明:所提出预测模型可较为合理地预测各维修周期内轨道质量发展,为研究轨道质量生命周期提供思路.%The grey nonlinear model put forward by the author (see the Journal of the China Railway Society, 2010,2) is viewed as a time function controlled by a series of parameters including trend coefficients and random coefficients. The coefficients are identification parameters for system analysis, which represent the characteristics that the system is varying with time. The prediction model is established under the conditions of knowing tamp operating efficiency of large machine or initial quality of railway track. The relevance among track quality systems in different maintenance periods is assumed, and then TQI characteristics in the maintenance period required predicting can be obtained by data mining on identification parameters of TQI in the known maintenance periods. Through the calculation example we can see the prediction model proposed in this paper can predict the development of track quality in different maintenance periods accurately and also provides a new idea for the research on life cycle of track quality.
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