摘要
文章通过研究几类灰色模型,提出Newton-cotes GM(1,N)数值算法,通过实例分析,Newton-cotes GM(1,N)模型比其他几类GM(1,N)模型预测精度高。为了克服这种灰色建模算法不能更改参数的缺点,提出了基于扰动因子改进的灰色模型;通过扰动因子的数值变化对参数的影响,达到改变特征因素的最优预测值;依据平均相对误差指标,对预测的结果进行误差分析和比较,得到新算法的拟合精度比原有算法的拟合精度有明显的改进。
Based on the study of several types of gray models, this paper proposes Newton-cotes GM(1, N) numerical algorithm. Case analyses show that Newton-cotes GM(1, N) model has higher prediction accuracy than other GM(1, N) models. In order to overcome the disadvantage that parameters can not be changed in the grey modeling algorithm, the paper proposes a new grey model based on perturbation factor improvement, and the proposed model is able to achieve the optimal prediction value of changing characteristic factors through the influence of the numerical change of the perturbation factor on the parameters. Finally,according to the average relative error index, the paper carries out error analysis and comparison of the predicted results, indicating that the fitting accuracy of the new algorithm is significantly improved than that of the original algorithm.
引文
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