改进无偏灰色模型在沉降观测中的应用

Improvement of Unbiased Grey-Forecasting Model and Its Application on Subsidence Prediction

摘要: 无偏灰色预测模型能消除传统灰色预测模型本身所固有的偏差，是无偏指数模型。原始数据经负指数函数变换后可改进原始数据的光滑度，提高灰色模型预测范围和精度。结合这两种方法得到基于指数变换的无偏灰色预测模型，将原始数据经过负指数变化成为指数变换序列，利用无偏灰色模型进行预测，还原预测值。将实测沉降监测点数据分别由灰色预测模型﹑无偏灰色预测模型和本文模型进行预测，实验结果表明本文改进模型消除了传统模型固有偏差，在沉降预测中取得了良好效果。

Abstract: The unbiased grey-forecasting model is an unbiased exponential model and it can eliminate inhe-rent deviation of the traditional grey-prediction model. After negative exponential function trans-forming, the smoothness of the original data and the prediction accuracy are both improved. It is the unbiased grey-forecasting model based on exponential transform that combines the unbiased grey-forecasting model and negative exponential transformation. The data is transformed, pre-dicted and restored. The subsidence monitoring data is processed by the grey-prediction model, the unbiased grey-prediction model and the model in this paper. The results show that the model in this paper eliminates the inherent deviation of the traditional model and achieves good results in the subsidence prediction.