{"title":"非线性小样本条件下几种常用的基于知识的智能故障诊断方法的比较","authors":"Zhu Ning","doi":"10.1109/ITME.2016.0035","DOIUrl":null,"url":null,"abstract":"This article compared several knowledge-based fault diagnosis methods which common used last ten years under nonlinear small sample condition. The main content focused on accuracy, speed, generalization and implementation. The comparison result of these methods showed the parameter optimizated surpport vector machines have higher efficiency and broad prospect.","PeriodicalId":184905,"journal":{"name":"2016 8th International Conference on Information Technology in Medicine and Education (ITME)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Comparison of Several Common Intelligent Fault Diagnosis Knowledge-Based Method under Nonlinear Small Sample Conditions\",\"authors\":\"Zhu Ning\",\"doi\":\"10.1109/ITME.2016.0035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article compared several knowledge-based fault diagnosis methods which common used last ten years under nonlinear small sample condition. The main content focused on accuracy, speed, generalization and implementation. The comparison result of these methods showed the parameter optimizated surpport vector machines have higher efficiency and broad prospect.\",\"PeriodicalId\":184905,\"journal\":{\"name\":\"2016 8th International Conference on Information Technology in Medicine and Education (ITME)\",\"volume\":\"151 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 8th International Conference on Information Technology in Medicine and Education (ITME)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITME.2016.0035\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 8th International Conference on Information Technology in Medicine and Education (ITME)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITME.2016.0035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of Several Common Intelligent Fault Diagnosis Knowledge-Based Method under Nonlinear Small Sample Conditions
This article compared several knowledge-based fault diagnosis methods which common used last ten years under nonlinear small sample condition. The main content focused on accuracy, speed, generalization and implementation. The comparison result of these methods showed the parameter optimizated surpport vector machines have higher efficiency and broad prospect.