PM2.5 Concentration Calibration Based on BP Neural Network and Fuzzy Membership Degree
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.
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