Research And Application Of New Information Accumulative GM(1,N) Model

The grey system theory has very good superiority of the application of small amount of data information and uncertainty problem research. The core content of the grey system theory is GM model,and many researchers also do a lot of work for GM(1,1) study, the results shows that GM(1,1) model belongs to a single sequence model, which is not applicable for the prediction of many existing complex systems,GM(1,N) belongs to a multifactor model,they study for how to improve GM(1,N) simulation and prediction accuracy is necessary.Acumulative method have the advantage for easy compute , high efficiency and accuracy, this paper provide the new information accumulative GM(1,N) model, which consist of accumulative GM(1,N) simulation and new inforamation accumulative GM(1,N)forecast model. We compelete the GM(1,N) parameter identification with accumulative method and simulate the GM(1,N) model. Considering the effect of different initial conditions function to the new information acumulative GM(1,1) model after the grey action optimization, we improved the model by using the extremum necessary conditions to determine the iniaial, at the same time put forward and proves that the optimization of the initial item is valid, so put the improved new information accumulative GM(1,N) model to the related extrapolation. We put date point k related sequence into derived model, to complete the prediction process of the main suquence date point k. Considering the new information factor, we give up the late data, we complete the main sequence prediction in turn after the date point k by repeating the above steps. The simulation shows that the new information accumulation GM(1,N) model in our paper is better than original GM(1,N)model, in both simulation capability and forecast precision.