中国资源综合利用2024，Vol.42Issue(4)：54-56,3.DOI:10.3969/j.issn.1008-9500.2024.04.016
基于BP神经网络和模糊隶属度的PM2.5浓度校准
PM2.5 Concentration Calibration Based on BP Neural Network and Fuzzy Membership Degree
周云1
作者信息
- 1. 信阳航空职业学院,河南 信阳 464000
- 折叠
摘要
Abstract
Based on a large amount of data,this paper uses Pearson correlation coefficient and fuzzy membership degree to analyze the correlation between fine particulate matter(PM2.5)concentration data of self built points and national control points.During this process,a Back Propagation(BP)neural network model is established for training,and the optimal algorithm and related parameters of the neural network are determined by using the ergodic trial and error method.After repeated debugging,the mean square error of the calibration results relative to the national control point data decreases to 0.005,and the equalization coefficient is 0.95,and the system shows excellent calibration performance.The research results indicate that after combining fuzzy membership degree preprocessing with raw data,selecting a suitable and structurally structured BP neural network for training can effectively calibrate the PM2.5 concentration data of self built points and improve the accuracy of self built point data.关键词
PM2.5 浓度/BP神经网络/模糊隶属度/校准Key words
PM2.5 concentration/Back Propagation(BP)neural network/fuzzy membership degree/calibration分类
信息技术与安全科学引用本文复制引用
周云..基于BP神经网络和模糊隶属度的PM2.5浓度校准[J].中国资源综合利用,2024,42(4):54-56,3.
