{"title":"基于模糊神经网络的融合算法","authors":"Alexey Natekin, A. Knoll","doi":"10.1109/WISP.2013.6657480","DOIUrl":null,"url":null,"abstract":"The problem of optimal fusion of several predictive machine learning regression models is considered. The method of combining different predictive models based on additive fuzzy systems is presented. The framework of model fusion based on fuzzy neural networks is described and the appropriate algorithms are derived. Learning process justifications and the requirement of the separate fusion set are discussed. The presented models are supported with the real world application example of robotic hand control.","PeriodicalId":350883,"journal":{"name":"2013 IEEE 8th International Symposium on Intelligent Signal Processing","volume":"136 31","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fusion algorithm based on fuzzy neural networks\",\"authors\":\"Alexey Natekin, A. Knoll\",\"doi\":\"10.1109/WISP.2013.6657480\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of optimal fusion of several predictive machine learning regression models is considered. The method of combining different predictive models based on additive fuzzy systems is presented. The framework of model fusion based on fuzzy neural networks is described and the appropriate algorithms are derived. Learning process justifications and the requirement of the separate fusion set are discussed. The presented models are supported with the real world application example of robotic hand control.\",\"PeriodicalId\":350883,\"journal\":{\"name\":\"2013 IEEE 8th International Symposium on Intelligent Signal Processing\",\"volume\":\"136 31\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE 8th International Symposium on Intelligent Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WISP.2013.6657480\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 8th International Symposium on Intelligent Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WISP.2013.6657480","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The problem of optimal fusion of several predictive machine learning regression models is considered. The method of combining different predictive models based on additive fuzzy systems is presented. The framework of model fusion based on fuzzy neural networks is described and the appropriate algorithms are derived. Learning process justifications and the requirement of the separate fusion set are discussed. The presented models are supported with the real world application example of robotic hand control.