摘要 针对UBGM(1,1)-Markov模型中存在2个邻近值可能被归属到不同状态，导致预测值产生偏差的问题，结合模糊分类理论，构建基于模糊分类的无偏灰色-马尔科夫模型(unbiased gray-Markov model based on fuzzy classification, FC-UBGM(1,1)-Markov)。首先对UBGM(1,1)模型进行残差修正，然后将修正后拟合值的相对残差序列作为Markov链进行区间划分，再结合模糊分类的隶属度函数，计算相对残差的模糊向量，根据隶属度确定其所属的状态。实际算例表明，该模型比传统UBGM(1,1)-Markov模型的预测效果更好。
Abstract：Predicting slope settlement in open-pit mines is an important means to grasp the trend of slope movement and guarantee the safe operation of the mine. Aiming at the problem that for the UBGM(1,1)-Markov model, two neighboring values may be assigned to different states, leading to deviations in predicted values; combined with fuzzy classification theory, we propose an unbiased grey-Markov model based on fuzzy classification model(FC-UBGM(1,1)-Markov). First, the residual correction is performed on the UBGM(1,1) model; the relative residual sequence of the fitted values after the correction is used as the Markov chain to divide the interval, and the membership function of the fuzzy classification is used to calculate the fuzzy vector of the relative residual. The accuracy of this prediction model is analyzed through actual cases. The experiment results show that, compared with the traditional UBGM(1,1)-Markov model, the predictive power of the model in this paper is better.