山东大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (6): 19-25.doi: 10.6040/j.issn.1672-3961.1.2014.180
郑毅, 朱成璋
ZHENG Yi, ZHU Chengzhang
摘要: 提出一种基于深度信念网络(deep belief networks, DBNs)的区域PM2.5日均值预测方法,讨论了训练数据选择方式,并优化了DBNs参数设置。通过相关实验并与基于径向基神经网络(radial basis function, RBF)和反向传播神经网络(back propagation, BP)方法比较,验证了基于DBNs方法的可行性和预测精度。实验结果表明:基于DBNs的方法,区域(西安市)预测PM2.5日均值与观测日均值之间均方差(mean square error, MSE)为8.47×10-4mg2/m6;而采用相同数据集,基于RBF和BP的方法均方差为1.30×10-3mg2/m6和1.96×10-3mg2/m6。比较分析表明:基于DBNs的方法能较好预测区域整体PM2.5的日均值变化趋势,显著优于基于神经网络和径向基网络方法的预测结果。
中图分类号:
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