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常规GM(1,1)模型基于最小二乘原理,当建模数据中含有粗差时,将对整个模型的估值产生较大影响。为此,将稳健估计引入灰色模型建模,提出了稳健动态GM(1,1)模型的建模方法。通过对静态GM、动态GM及稳健动态GM进行建模,利用MATLAB编程对实测数据进行了验证计算分析。结果表明,当监测序列含有粗差时,稳健动态GM(1,1)模型相对其他模型能有效抵抗粗差的影响,预报精度也有较明显的提高。
The conventional GM (1,1) model is based on the principle of least squares. When the model data contains gross errors, it will have a great impact on the estimation of the entire model. Therefore, the robust estimation is introduced into the gray model modeling, and the robust dynamic GM (1,1) model modeling method is proposed. Through the static GM, dynamic GM and steady dynamic GM modeling, the use of MATLAB programming to verify the measured data analysis. The results show that when the monitoring sequence contains gross errors, the robust dynamic GM (1,1) model can effectively resist the effects of gross errors compared with other models, and the prediction accuracy is also improved obviously.