一种基于BP神经网络改进算法的PM_(2.5)预测方法

An Improved PM_(2.5) Prediction Model based on BP Neural Network

针对BP神经网络进行空气污染预测收敛速度过慢和学习过程中易出现"过适配"的问题,提出了利用主成分分析和提前终止训练法对传统BP模型进行改进的新算法。并利用MATLAB平台进行了仿真研究,结果表明:改进后的算法提高了BP神经网络的预测精度和泛化能力,能较好地实现PM2.5浓度预测。

Aimed at solving the problems of low convergence rate in the prediciton of the air pollution by BP neural network and the ＂over- adaptation＂ during study, this paper proposed a new algorithm, which used principle components analysis and early termination training method to improve conventional BP algorithm. Simulation study is carried out on the platform of MATLAB. The result showed that the improved algorithm could increase prediction accuracy and enhanced the generalization ability of BP neural network. Thus, it can better predict the concentration of PM2.5.