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本文提出了基于GRNN神经网络对煤炭价格波动进行预测的理论,通过GRNN神经网络,利用MATLAB和WEKA等软件以及粗糙集等理论分析并验证了国内生产总值、煤炭生产总量、消费总量等因素对煤炭价格波动的影响。成功实现了对煤炭价格波动基于GRNN神经网络的训练和预测;建立了煤炭价格波动的预测模型,大大提高了预测的准确度;基于GRNN神经网络的预测结果准确率为87.5%。结果表明:GRNN神经网络对煤炭价格波动的预测是较成功的。
This paper presents a theory of forecasting coal price volatility based on GRNN neural network. Through GRNN neural network, software such as MATLAB and WEKA and rough set theory, the paper analyzes and verifies GDP, total coal production, total consumption, etc. Influences of Factors on Coal Price Fluctuation. The training and forecasting of coal price fluctuation based on GRNN neural network has been successfully achieved. The prediction model of coal price volatility has been set up and the accuracy of prediction has been greatly improved. The accuracy of the forecasting result based on GRNN neural network is 87.5%. The results show that the prediction of coal price volatility by GRNN neural network is more successful.