{"title":"改进的EMD-PSO-LSSVM列车无线网络时延预测","authors":"S. Dou, L. Y. Zhang, C. X. Li","doi":"10.1117/12.2655218","DOIUrl":null,"url":null,"abstract":"A time delay prediction method of train network based on wireless transmission is proposed. EMD is used to decompose the time delay series. The decomposed components with large sample entropy are DWT to form new components, in order to reduce the complexity of prediction. The components with similar sample entropy are combined into new components to reduce the amount of model calculation. Finally, each data component is predicted by particle swarm optimization LSSVM model. The simulation results show that the proposed method has high prediction accuracy.","PeriodicalId":105577,"journal":{"name":"International Conference on Signal Processing and Communication Security","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Improved EMD-PSO-LSSVM train wireless network time-delay prediction\",\"authors\":\"S. Dou, L. Y. Zhang, C. X. Li\",\"doi\":\"10.1117/12.2655218\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A time delay prediction method of train network based on wireless transmission is proposed. EMD is used to decompose the time delay series. The decomposed components with large sample entropy are DWT to form new components, in order to reduce the complexity of prediction. The components with similar sample entropy are combined into new components to reduce the amount of model calculation. Finally, each data component is predicted by particle swarm optimization LSSVM model. The simulation results show that the proposed method has high prediction accuracy.\",\"PeriodicalId\":105577,\"journal\":{\"name\":\"International Conference on Signal Processing and Communication Security\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Signal Processing and Communication Security\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2655218\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Signal Processing and Communication Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2655218","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A time delay prediction method of train network based on wireless transmission is proposed. EMD is used to decompose the time delay series. The decomposed components with large sample entropy are DWT to form new components, in order to reduce the complexity of prediction. The components with similar sample entropy are combined into new components to reduce the amount of model calculation. Finally, each data component is predicted by particle swarm optimization LSSVM model. The simulation results show that the proposed method has high prediction accuracy.