加权马尔可夫SCGM(1,1)c模型在干旱作物面积预测中的应用

Xiang-cheng JIANG , Sen-fa CHEN
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引用次数: 14

摘要

干旱作物面积预测是我国农业发展的基础。根据灰色系统理论和马尔可夫链原理,采用单基因系统云灰色SCGM(1,1)c模型拟合少数时间序列的发展趋势,其误差指标是随机波动的。马尔可夫链法适用于随机波动动态过程的预测,选择加权马尔可夫链来预测误差指标。结合两种模型的优点,建立了一种加权SCGM(1,1)c模型,该模型适用于此类系统在短时间内、数据量少、随机波动动态过程的干旱作物面积预测。实例表明,加权SCGM(1,1)c模型对干旱作物面积具有较高的预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Weighted Markov SCGM(1,1)c Model to Predict Drought Crop Area

The prediction of drought crop area is the basis of agricultural development in our country. According to grey system theory and Markov chain principle, applying a single gene system cloud grey SCGM(1,1)c model to fit the development tendency of the few time series, its error index is stochastically fluctuated. Markov chain method is suitable for forecasting stochastic fluctuating dynamic process, selecting weighted Markov chain to predict the error index. Combining the advantages of the two models, we found a weighted SCGM(1,1)c model for drought crop area prediction, and the new model is suitable for forecasting such kinds of system with in a short time, with few data, and stochastic fluctuating dynamic process. The example shows that the weighted SCGM(1,1)c model can have high prediction precision for drought crop area.

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