灰色优化GM(1,1)和人工神经网络组合模型的江西省GDP预测应用

为了提高GDP的预测精度,结合灰色系统和人工神经网络的各自优势,建立灰色人工神经网络组合预测模型。该模型既具有灰色优化GM(1,1)模型适用发展系数范围较大的优点,也融合了人工神经网络在不确定因素预测方面的优点。最后以江西省GDP的预测为实例,对比了单独的灰色优化GM(1,1)模型与组合模型的预测结果,结果显示组合模型的预测精度较高。

Gray Optimized GM （1, 1） and Artificial Neural Network Combined Model for GDP Prediction of Jiangxi Province

In order to improve the prediction precision of GDP, this paper combines with grey system and aritificial neural network to establish the gray aritificial neural network combination forecast model.This model, with gray optimized GM （1,1） model, is applicable to a wider range of development coefficient, but also combines the advantages of artificial neural networks in the field of uncertainties in forecasting. Finally, the author takes the GDP of Jiangxi Province for instance and compares the single gray optimized GM （1,1） model with the combination forecasting model, the results show that the combination forecasting model has a higher precision.