Abstract: In view of the traditional GM（1，1） model prediction accuracy is not high, additionally its solving method optimizing and polynomial fitting both have the one-sided ness problem, this paper presents the grey dynamic equal dimension forecasting method based on solving method optimizing and polynomial fitting. Then according to the the statistical data of population in the United States in nearly two hundred years, it can compare the error by the method of using the traditional GM（1，1） model and its optimization model. The results show that the improved grey model has higher prediction accuracy, indicating that the improved grey forecasting model with the better reliability and feasibility.

Key words: grey prediction, solving method optimizing, polynomial fitting, equal dimension grey number

摘要： 针对传统的GM（1，1）模型预测精度不高，并且其求解优化与多项式拟合各有片面性的缺点，给出了基于求解优化和多项式拟合优化相结合的改进灰色等维动态预测方法。结合美国近两百年人口的相关统计数据，利用传统的GM（1，1）模型及其优化后的模型进行误差比较。结果表明改进后的灰色模型预测精度更高，说明改进后的灰色预测模型的可行性与可靠性更好。

关键词: 灰色预测, 求解优化, 多项式拟合, 灰色等维递补