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| PCA-BP神经网络在SO2浓度预报中的应用 | |
| 引用本文： | 于文革,王体健,杨诚,孙莹.PCA-BP神经网络在SO2浓度预报中的应用[J].气象,2008,34(6):97-101. |
| 作者姓名： | 于文革 王体健 杨诚 孙莹 |
| 作者单位： | 1. 南京大学大气科学系,210093;辽宁省丹东市气象台 2. 南京大学大气科学系,210093 3. 辽宁省丹东市气象台 |
| 摘 要： | 将基于主成分分析(PCA)的BP神经网络预报方法引入大气污染预报,建立SO2浓度预报模型.结果表明:应用主成分分析对数据进行前处理,以原始预报因子的主成分作为BP神经网络的输入,降低了数据维数,消除了样本间存在的相关性,大大加快了BP神经网络的收敛速度.对模型进行预报验证,预报值与实际值之间的绝对误差为0.0098,预报值与实际值的相关系数达到0.885,得到较好的预报效果.并且比一般的BP神经网络模型具有较高的拟合和预报精度. |
| 关 键 词： | 主成分分析 BP神经网络 大气污染 SO2浓度预报 |
| 收稿时间： | 2007/11/27 0:00:00 |
| 修稿时间： | 2007年11月27 |
Application of PCA-BP Neural Network to SO2 Concentration Forecast |
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| Yu Wenge,Wang Tijian,Yang Cheng and Sun Ying.Application of PCA-BP Neural Network to SO2 Concentration Forecast[J].Meteorological Monthly,2008,34(6):97-101. | |
| Authors: | Yu Wenge Wang Tijian Yang Cheng and Sun Ying |
| Abstract: | Based on principal components analysis (PCA),the BP (Back Propagation) neural ne twork forecast method is introduced in air pollution prediction and the SO2 co ncentration prediction model is established. The results indicate that by applyi ng the principal component analysis in the data pre processing and taking the principal components of primitive predictor as the input of neural network, it can reduce the dimension of data, eliminate the correlation between the samples, and largel y speed up the convergence rate. The verification of forecast model shows that t he absolute error between the forecasts and the real value is 0.0098, and the co rrelation coefficient between them reaches 0.885. The PCA BP model has a fit ac curacy better than the common BP model. |
| Keywords: | principal components analysis BP neural network air pollution SO2 concentra tion forecasting |
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