Prediction of calibration interval for a measuring instrument using rolling grey bootstrap fusion model
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Abstract:
A rolling grey bootstrap fusion model (RGBFM(1,1)) is proposed to predict calibration interval of a measuring instrument under small sample. The model combines GM(1,1) model with bootstrap method. Bootstrap re-sampling is used in the process of modeling the grey differential coefficient function to mine more information about systems. Both the instantaneous value and interval assessment values can be predicted using RGBFM(1,1), which can reduce prediction risk of calibration interval. In contrast, other prediction models only predict the instantaneous value. Experiments show that the RGBFM(1,1) can exactly describe the random wave of original sample data in prediction of instantaneous value, interval upper limit and lower limit, and has higher prediction reliability. Therefore, the RGBFM(1,1) is suitable for the prediction of calibration interval for a measuring instrument.
