{"title":"LDHA-BP在大气PM2.5浓度预测中的应用","authors":"Jianqiang Zhao, J. Dou, Kao Ge","doi":"10.1109/ITNEC.2019.8729040","DOIUrl":null,"url":null,"abstract":"LDHA-BP algorithm is designed for lack of using BP neural networks to predict atmospheric concentrations of PM2.5. In this algorithm, the process of solving the optimal weights and thresholds of BP neural networks will be transformed into the process of finding the optimal location of the dolphin species predator's, by introducing Leader Dolphins Herd Algorithm (LDHA).The algorithm effectively combines the good generalization ability of BP neural network neural and the global optimization ability, local search capability of LDHA. Test data for PM2.5 from a monitoring point in Wuhan to predict the PM2.5 concentrations in future. The experimental results show that LDHA-BP is faster, more accurate to predict the PM2.5 concentrations and it has good practical value.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Application of LDHA-BP in Prediction of Atmospheric PM2.5 Concentration\",\"authors\":\"Jianqiang Zhao, J. Dou, Kao Ge\",\"doi\":\"10.1109/ITNEC.2019.8729040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"LDHA-BP algorithm is designed for lack of using BP neural networks to predict atmospheric concentrations of PM2.5. In this algorithm, the process of solving the optimal weights and thresholds of BP neural networks will be transformed into the process of finding the optimal location of the dolphin species predator's, by introducing Leader Dolphins Herd Algorithm (LDHA).The algorithm effectively combines the good generalization ability of BP neural network neural and the global optimization ability, local search capability of LDHA. Test data for PM2.5 from a monitoring point in Wuhan to predict the PM2.5 concentrations in future. The experimental results show that LDHA-BP is faster, more accurate to predict the PM2.5 concentrations and it has good practical value.\",\"PeriodicalId\":202966,\"journal\":{\"name\":\"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)\",\"volume\":\"106 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITNEC.2019.8729040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITNEC.2019.8729040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of LDHA-BP in Prediction of Atmospheric PM2.5 Concentration
LDHA-BP algorithm is designed for lack of using BP neural networks to predict atmospheric concentrations of PM2.5. In this algorithm, the process of solving the optimal weights and thresholds of BP neural networks will be transformed into the process of finding the optimal location of the dolphin species predator's, by introducing Leader Dolphins Herd Algorithm (LDHA).The algorithm effectively combines the good generalization ability of BP neural network neural and the global optimization ability, local search capability of LDHA. Test data for PM2.5 from a monitoring point in Wuhan to predict the PM2.5 concentrations in future. The experimental results show that LDHA-BP is faster, more accurate to predict the PM2.5 concentrations and it has good practical value.