【摘要】 对变化较平稳的数据和变化幅度较大的非平稳数据两种序列建立的 GM(1 ,1 )模型 ,分别用加速遗传算法 (AGA)和最小二乘法 (LSM)对模型参数求解 .结果表明 ,对变化较平稳数据序列 ,两种参数求解法建立的预测模型的拟合优度和预测精度相差无几 ;对变化幅度较大的非平稳数据序列 ,基于 AGA的 GM(1 ,1 )模型的拟合优度和预测精度远高于基于 LSM的 GM(1 ,1 )模型的拟合优度和预测精度 .更多还原

【Abstract】 Accelerating genetic algorithm (AGA) and least square method(LSM) were used to solving the parameters of gray GM(1,1) model for the modellings of stable data sequence and unstable deta sequence, respectively. It is shown that there is little difference of the precisions of model to solving parameters between AGA and LSM for stable data sequence, but the precision of fitting and forecasting of model to solving parameters based on AGA is much more than that of LSM for unstable data sequence.更多还原