基于新陈代谢GM(1,1)模型的校准间隔预测

Prediction of Calibration Intervals Based on the Renewal GM(1,1) Model

针对测量仪器校准间隔的优化问题,根据校准数据非线性、小样本的特点,提出了一种基于新陈代谢GM(1,1)模型的校准间隔预测方法.通过分析历史校准数据的特征,建立了新陈代谢GM(1,1)预测模型,通过仿真实验对预测模型进行了对比验证.结果表明,相对于灰色GM(1,1)模型,新陈代谢GM(1,1)模型克服了随机扰动对系统的影响,更能反映系统的变化趋势,预测精度更高,适合用于测量仪器校准间隔的预测.

To optimize the calibration interval of a measuring instrument, a prediction method based on the renewal GM （1,1） model is put forward. Supposing that the calibration status of a measuring instrument can be predicted by the instrument＇s historical calibration data, the renewal GM （1,1） model is developed according to the characters of calibration data, such as nonlinear feature and small amount of samples. The results of simulating experiments demonstrate that compared with the gray GM （1,1） model, the renewal GM （1,1） model conquers the effect of random disturbance and has higher prediction precision. Therefore, the model can be used to forecast the calibration interval of a measuring instrument.