{"title":"Application study on BP neural network in the incidence rate of pulmonary emphysema","authors":"Ma Liang-liang, Tian Fu-peng","doi":"10.1109/ICAIE.2010.5641480","DOIUrl":null,"url":null,"abstract":"The connotation of BP neural network algorithm and code was introduced. The construction of expert system for forecasting the incidence rate of pulmonary emphysema based on BP neural network was discussed. Methods The data of incidence rate of pulmonary emphysema and meteorological factors in plateau section from 2003 to 2009 were collected and analyzed by using Eviews for windows, version 3.1; the model of Back Propagation artificial neural network was built by Matlab, version7.0. The MER and R2 of pulmonary emphysema incidence rate forecasting model were 0.60401% and 0.95329 respectively. The effect of fitting and forecasting of the model were very well. BP neural network had a strong application value in the forecasting the incidence rate of pulmonary emphysema.","PeriodicalId":216006,"journal":{"name":"2010 International Conference on Artificial Intelligence and Education (ICAIE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Artificial Intelligence and Education (ICAIE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIE.2010.5641480","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The connotation of BP neural network algorithm and code was introduced. The construction of expert system for forecasting the incidence rate of pulmonary emphysema based on BP neural network was discussed. Methods The data of incidence rate of pulmonary emphysema and meteorological factors in plateau section from 2003 to 2009 were collected and analyzed by using Eviews for windows, version 3.1; the model of Back Propagation artificial neural network was built by Matlab, version7.0. The MER and R2 of pulmonary emphysema incidence rate forecasting model were 0.60401% and 0.95329 respectively. The effect of fitting and forecasting of the model were very well. BP neural network had a strong application value in the forecasting the incidence rate of pulmonary emphysema.
介绍了BP神经网络算法的内涵和代码。探讨了基于BP神经网络的肺气肿发病率预测专家系统的构建。方法采用Eviews for windows 3.1版软件,收集2003 - 2009年高原地区肺气肿发病率及气象因素资料进行分析;利用Matlab 7.0版本建立了反向传播人工神经网络模型。肺气肿发病率预测模型的MER和R2分别为0.60401%和0.95329。模型的拟合和预测效果良好。BP神经网络在预测肺气肿发病率方面具有较强的应用价值。