The deterministic grey dynamic model with convolution integral DGDMC(1, n)

Abstract

Although the grey forecasting models have been successfully utilized in many fields and demonstrated promising results, literatures show their performance still could be improved. The grey prediction theory is methodology and it is necessary to constantly present new models or algorithm based on the theory to improve its performance, prediction accuracy especially. For this purpose, this paper proposes a new prediction model called the deterministic grey dynamic model with convolution integral, abbreviated as DGDMC(1, n). Improvements upon the existing grey prediction model GM(1, n) are made to a large extent and the messages for a system can be inserted sufficiently. The major improvements include determining the unbiased estimates of the system parameters by the deterministic convergence scheme, introducing the first derivative of the 1-AGO data of each associated series into the DGDMC(1, n) model to strengthen the indicative significance and evaluating the modelling 1-AGO data of the predicted series by the convolution integral. The indirect measurement of the tensile strength of a material for a higher temperature is adpoted for demonstration. The results show that the accuracy of indirect measurement is higher by the DGDMC(1, n) model than by the existing GM(1, n) model.