Cai Guoqiang, Jia Limin, Y. Jianwei, L. Haibo, Li Xi
{"title":"多变量相关分析的数量建模与应用","authors":"Cai Guoqiang, Jia Limin, Y. Jianwei, L. Haibo, Li Xi","doi":"10.1109/ICCIT.2009.320","DOIUrl":null,"url":null,"abstract":"Abstract-This study focuses on quantitative correlation problem of four Railway Parcel traffic parameters: Number of Initial trains (NIT), GDP of cities, Number of Parcel Traffic Agencies (NPTA) and Number of Parcel traffic Nodes (NPTN). It can be seen as a multivariable systems that called Multiple-Input Single-Output(MISO). Then ANN is used in to resolve the multivariable Correlation Analysis problems in China Railway Parcel forecast. Based on Artificial Neural Networks (ANN), the prediction of China Railway Parcel Traffic Volume is modeling. The model can effectively solve the variable multiple correlation problem. Good performance is demonstrated when Application proves the accuracy of the model and its contribution.","PeriodicalId":112416,"journal":{"name":"2009 Fourth International Conference on Computer Sciences and Convergence Information Technology","volume":"93 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantity Modeling and Application of Multivariable Correlation Analysis\",\"authors\":\"Cai Guoqiang, Jia Limin, Y. Jianwei, L. Haibo, Li Xi\",\"doi\":\"10.1109/ICCIT.2009.320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract-This study focuses on quantitative correlation problem of four Railway Parcel traffic parameters: Number of Initial trains (NIT), GDP of cities, Number of Parcel Traffic Agencies (NPTA) and Number of Parcel traffic Nodes (NPTN). It can be seen as a multivariable systems that called Multiple-Input Single-Output(MISO). Then ANN is used in to resolve the multivariable Correlation Analysis problems in China Railway Parcel forecast. Based on Artificial Neural Networks (ANN), the prediction of China Railway Parcel Traffic Volume is modeling. The model can effectively solve the variable multiple correlation problem. Good performance is demonstrated when Application proves the accuracy of the model and its contribution.\",\"PeriodicalId\":112416,\"journal\":{\"name\":\"2009 Fourth International Conference on Computer Sciences and Convergence Information Technology\",\"volume\":\"93 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-11-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Fourth International Conference on Computer Sciences and Convergence Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCIT.2009.320\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Fourth International Conference on Computer Sciences and Convergence Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIT.2009.320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quantity Modeling and Application of Multivariable Correlation Analysis
Abstract-This study focuses on quantitative correlation problem of four Railway Parcel traffic parameters: Number of Initial trains (NIT), GDP of cities, Number of Parcel Traffic Agencies (NPTA) and Number of Parcel traffic Nodes (NPTN). It can be seen as a multivariable systems that called Multiple-Input Single-Output(MISO). Then ANN is used in to resolve the multivariable Correlation Analysis problems in China Railway Parcel forecast. Based on Artificial Neural Networks (ANN), the prediction of China Railway Parcel Traffic Volume is modeling. The model can effectively solve the variable multiple correlation problem. Good performance is demonstrated when Application proves the accuracy of the model and its contribution.