The Increased Grey Model And Its Applications On Population Prediction

The number of population is an important index to reflect the development level for economy and scale the social advancement. We grasp the number of population and know its developmental trend exactly, which will be of profound infection establishing the plan of national economy and tactic of social development. China has the first largest number of population in the world, which possesses one fifth of the world population. The problem of large population is the chief factor restricting Chinese economy and social advancement. This paper applies the grey system theory to predict and analyse the future population in China, which provide referenced suggestions for establishing population and economical policy.The Grey System Theory was founded by Prof. J. L. Deng in 1982, which studies the uncertain system on the "small sample", "poor information", i.e., "part information known, part information unknown" we have. Through creating and excavating the limited information, we could understand the real world, describe its evolvement rule and grasp its running behavior exactly. On the basis of grey system theory, this paper improves the grey model, i.e., add a process of first-order accumulative subtraction for the purpose of separating the increment part. This change strengthen increment information prominence, weaken disturbing factors and reveal the operational law of the system ,thus higher precision can be gained. At the same time ,the author unite the latest research production, put the increment concept into new initial value grey and dispersed grey model, we call NGIM and DGIM for short. The results show higher precision on China's population prediction according to the latest data of China population. In order to validate the impression of grey increment model on population prediction, the author set examples of Jiangsu province and the city of Shanghai, which are the most developed region in East China. And make predictions and analysis for their future population according to the latest data. The results show that grey increment models will maintain high predicting accuracy in mid- and long-range predictions. Besides, no large quantities of data are demanded in collection, four to eight samples can be chosen for modeling which is useful especially when data are difficult to obtain. And it is flexible in model operation with small calculations. Consequently, the grey increment model is a useful new tool for population prediction.