{"title":"神经网络非线性组合预测方法及其应用","authors":"Hu Ping","doi":"10.1109/CESCE.2010.118","DOIUrl":null,"url":null,"abstract":"This paper adopted the artificial neural network, and also introduced the combination forecasting theory, established non-linear combination forecasting model based on artificial neural networks for hydrological time series forecasting. Though practical case calculating, and using the relevant evaluation index to analysis predict results, the results showed that non-linear combination forecasting method based on artificial neural network achieved better results than the original individual forecasting method.","PeriodicalId":6371,"journal":{"name":"2010 International Conference on Challenges in Environmental Science and Computer Engineering","volume":"80 1","pages":"18-21"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural Network Non-linear Combination Forecasting Method and Its Application\",\"authors\":\"Hu Ping\",\"doi\":\"10.1109/CESCE.2010.118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper adopted the artificial neural network, and also introduced the combination forecasting theory, established non-linear combination forecasting model based on artificial neural networks for hydrological time series forecasting. Though practical case calculating, and using the relevant evaluation index to analysis predict results, the results showed that non-linear combination forecasting method based on artificial neural network achieved better results than the original individual forecasting method.\",\"PeriodicalId\":6371,\"journal\":{\"name\":\"2010 International Conference on Challenges in Environmental Science and Computer Engineering\",\"volume\":\"80 1\",\"pages\":\"18-21\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Challenges in Environmental Science and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CESCE.2010.118\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Challenges in Environmental Science and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CESCE.2010.118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural Network Non-linear Combination Forecasting Method and Its Application
This paper adopted the artificial neural network, and also introduced the combination forecasting theory, established non-linear combination forecasting model based on artificial neural networks for hydrological time series forecasting. Though practical case calculating, and using the relevant evaluation index to analysis predict results, the results showed that non-linear combination forecasting method based on artificial neural network achieved better results than the original individual forecasting method.