Zhuo Yang, Shaojia Huang, Gongrui Sun, Zhenyu Deng
{"title":"基于在线时序增量和递减最小二乘支持向量机的汽车发动机建模","authors":"Zhuo Yang, Shaojia Huang, Gongrui Sun, Zhenyu Deng","doi":"10.1109/WARTIA.2014.6976328","DOIUrl":null,"url":null,"abstract":"Air-ratio relates closely to engine emissions, power and fuel consumption among all of the engine parameters. The thesis proposed an online time-sequence incremental and decremental least-squares support vector machines (OLSSVM) for engine modelling to predict the air-ratio. Experimental results show that the proposed OLSSVM can effectively predict the air-ratio to the target values under varies operating conditions and is superior to the air-ratio models available in the recent literatures. Therefore, the proposed OLSSVM is a promising scheme for automotive engine modelling.","PeriodicalId":288854,"journal":{"name":"2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Automotive engine modelling based on online time-sequence incremental and decremental least-squares support vector machines\",\"authors\":\"Zhuo Yang, Shaojia Huang, Gongrui Sun, Zhenyu Deng\",\"doi\":\"10.1109/WARTIA.2014.6976328\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Air-ratio relates closely to engine emissions, power and fuel consumption among all of the engine parameters. The thesis proposed an online time-sequence incremental and decremental least-squares support vector machines (OLSSVM) for engine modelling to predict the air-ratio. Experimental results show that the proposed OLSSVM can effectively predict the air-ratio to the target values under varies operating conditions and is superior to the air-ratio models available in the recent literatures. Therefore, the proposed OLSSVM is a promising scheme for automotive engine modelling.\",\"PeriodicalId\":288854,\"journal\":{\"name\":\"2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)\",\"volume\":\"99 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WARTIA.2014.6976328\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WARTIA.2014.6976328","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automotive engine modelling based on online time-sequence incremental and decremental least-squares support vector machines
Air-ratio relates closely to engine emissions, power and fuel consumption among all of the engine parameters. The thesis proposed an online time-sequence incremental and decremental least-squares support vector machines (OLSSVM) for engine modelling to predict the air-ratio. Experimental results show that the proposed OLSSVM can effectively predict the air-ratio to the target values under varies operating conditions and is superior to the air-ratio models available in the recent literatures. Therefore, the proposed OLSSVM is a promising scheme for automotive engine modelling.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
