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A Combined Forecasting Method of Grain Yield in China Based on GM(1,1) and BP Network

机译:基于GM(1,1)和BP网络的中国粮食产量组合预测方法。

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In view of the fact that the system of grain yield is affected by many factors and has complicated non-linear characteristic, a combined forecasting model by using multi-indicator for grain yield in China is constructed based on BP network and grey system, which can be named GM(1, 1)ȁ3;BP model. Seven index were chosen from agricultural production conditions, he primitive data of the multi-factors from 1980 to 2006 are taken as the input of BP network, The primitive data of the grain yield from 1980 to 2006 are taken as the output. Then the network structure, initial weighted values and thresholds are set. Taking the forecasting results of GM(1, 1) models for every factor from 2007 to 2015 as the input of BP network, the corresponding output of simulation are the forecasting results of the GM(1, 1)ȁ3;BP model, that is the grain yield from 2007 to 2015. The data from 2007 to 2008 are used as test sets, empirical results show that the combined model has higher precision and training efficiency than the models based on GM(1, 1), BP network or GM(1, N) alone.
机译:鉴于粮食产量体系受多种因素影响,具有非线性特征复杂的特点,基于BP网络和灰色系统构建了我国粮食产量多指标组合预测模型,可以被命名为GM(1,1)ȁ3; BP模型。从农业生产条件中选择了七个指标,以1980年至2006年的多因素原始数据作为BP网络的输入,以1980年至2006年的粮食单产原始数据作为输出。然后设置网络结构,初始加权值和阈值。以2007年至2015年每个因子的GM(1,1)模型的预测结果作为BP网络的输入,相应的模拟输出为GM(1,1)ȁ3; BP模型的预测结果。以2007年至2015年的粮食产量为基础。以2007年至2008年的数据作为检验集,实证结果表明,与基于GM(1,1),BP网络或GM()的模型相比,组合模型具有更高的精度和训练效率。 1,N)一个人。

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