Error Predicting for Dynamic Measurement of Poor Information Based on Grey Bootstrap Method

Abstract:

Different from traditional methods, a novel poor information measurement error prediction method based on grey system theory and bootstrap theory was presented. At first, all calibrated measurement error sources were calibrated, and all measurement error transfer coefficients were calculated, and the calibration data of all error sources were sampled based on bootstrap theory, and predictions of calibration data of all error sources were gained by a grey bootstrap fusion model. Then the error prediction values for dynamic measurement of poor information were got in terms of error combination principle. At last, in an example of a general dynamic measurement, the predicting measurement errors were acquired by this novel proposed method and the actual measurement errors were shown to be in a good agreement with each other, and the validity of the proposed method was also represented.