非等间隔灰色预测模型的二次优化

Re-optimization of non-equilditant gray model

为提高非等间隔序列数据的预测精度,采用一种新的加权方法对非等间隔序列数据进行处理,将其化为等间隔序列,在此基础之上使用初始条件的自适应性寻优函数对模型进行二次优化,得到最终的灰色预测模型.并用此模型对实测数据进行建模、分析和预测,计算残差平方和的平均数MSE的来比较该模型与其他模型的精度情况.研究结果表明:该模型在非等间隔数据处理中具有较好的预测精度.

In order to improve the prediction accuracy of non-equilditant sequence data, this paper used a new weighted method of non-equilditant sequence data and turned it into the equal interval series. Based on this, the model of initial conditions adaptive optimization function was used to quadratic optimization,-and finally grey forecasting model was obtained. And this model used for modeling, analysis, prediction and calculation of squared residuals and average MSE with the actual measured data, and comparing the accuracy of this model and other models. The results show that the model in the non-equilditant sequence data processing has better prediction accuracy.