{"title":"一种基于物种混合算法的神经模糊分类系统设计","authors":"Ching-Hung Lee, Hsin-Wei Chiu, Chung-Ta Li","doi":"10.1109/ICMLC.2010.5580807","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel neuro-fuzzy classification system by a species-based hybrid of electromagnetism-like mechanism and back-propagation algorithms (SEMBP). The neuro-fuzzy classification system is constructed by an interval type-2 fuzzy neural system with asymmetric membership functions (AIT2FNS). The hybrid algorithm SEMBP combines the advantages of EM and BP algorithms. Three classification problems: the XOR data set, the breast cancer data set, and the Iris data set are used to illustrate the performance of our approach.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A novel neuro-fuzzy classification system design by a species-based hybrid algorithm\",\"authors\":\"Ching-Hung Lee, Hsin-Wei Chiu, Chung-Ta Li\",\"doi\":\"10.1109/ICMLC.2010.5580807\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a novel neuro-fuzzy classification system by a species-based hybrid of electromagnetism-like mechanism and back-propagation algorithms (SEMBP). The neuro-fuzzy classification system is constructed by an interval type-2 fuzzy neural system with asymmetric membership functions (AIT2FNS). The hybrid algorithm SEMBP combines the advantages of EM and BP algorithms. Three classification problems: the XOR data set, the breast cancer data set, and the Iris data set are used to illustrate the performance of our approach.\",\"PeriodicalId\":126080,\"journal\":{\"name\":\"2010 International Conference on Machine Learning and Cybernetics\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2010.5580807\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2010.5580807","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A novel neuro-fuzzy classification system design by a species-based hybrid algorithm
In this paper, we propose a novel neuro-fuzzy classification system by a species-based hybrid of electromagnetism-like mechanism and back-propagation algorithms (SEMBP). The neuro-fuzzy classification system is constructed by an interval type-2 fuzzy neural system with asymmetric membership functions (AIT2FNS). The hybrid algorithm SEMBP combines the advantages of EM and BP algorithms. Three classification problems: the XOR data set, the breast cancer data set, and the Iris data set are used to illustrate the performance of our approach.
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