Methods to Improve the Generalization of BP Neural Network Applied in Air Pollution Forecasting
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摘要: 为了通过预测大气环境的质量和发展变化,来寻求有效地控制和改善环境质量的相应措施,选用英国伦敦马里波恩监测站PM2.5的小时平均浓度监测资料,采用贝叶斯归一化训练算法和提前终止法泛化改进的BP神经网络模型,预报PM2.5的24 h内的各小时浓度.结果表明,采用本方法进行空气污染预报,预测相对误差为20%~49%,提高了预报网络的泛化能力.Abstract: Atmospheric pollution prediction is helpful to find effective ways to control air pollution and improve air quality.BP neural network which was trained using Bayesian Regularization method and early stopping method was used to forecast the hourly concentration of PM2.5.The data of PM2.5 hourly concentration were obtained from monitoring site of Marylebone Road in London,UK.The prediction relative error goes from 20% to 49%.The results show that compared with networks which were trained using other methods, Bayesian Regularization method and early stopping method can improve the generalization ability of BP neural network.
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Keywords:
- back propagation network /
- generalisation /
- PM2.5 /
- pollution prediction
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