{"title":"直觉型2型模糊推理系统在电子学习系统中的应用","authors":"Kuo-Ping Lin, Yu-Ming Lu","doi":"10.1109/UMEDIA.2015.7297470","DOIUrl":null,"url":null,"abstract":"This study applies an intuitionistic type-2 fuzzy inference system (FIS) to e-learning system. This study utilizes the advantages of intuitionistic fuzzy set and type-2 fuzzy inference system, which have been proven to be most effective in uncertain environment, to recommend e-learning courses. The learning readiness is difficultly found in e-leaning company. In the study, triangular fuzzy numbers (TFS) are used to represent uncertain data. Intuitionistic type-2 is employed to model vaguely defined relations between TFS. The numerical example is used to verify the intuitionistic type-2 can effectively infer the learning readiness, and recommend more objective e-learning courses than traditional fuzzy inference systems.","PeriodicalId":262562,"journal":{"name":"2015 8th International Conference on Ubi-Media Computing (UMEDIA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Applying intuitionistic type-2 fuzzy inference system for e-learning system\",\"authors\":\"Kuo-Ping Lin, Yu-Ming Lu\",\"doi\":\"10.1109/UMEDIA.2015.7297470\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study applies an intuitionistic type-2 fuzzy inference system (FIS) to e-learning system. This study utilizes the advantages of intuitionistic fuzzy set and type-2 fuzzy inference system, which have been proven to be most effective in uncertain environment, to recommend e-learning courses. The learning readiness is difficultly found in e-leaning company. In the study, triangular fuzzy numbers (TFS) are used to represent uncertain data. Intuitionistic type-2 is employed to model vaguely defined relations between TFS. The numerical example is used to verify the intuitionistic type-2 can effectively infer the learning readiness, and recommend more objective e-learning courses than traditional fuzzy inference systems.\",\"PeriodicalId\":262562,\"journal\":{\"name\":\"2015 8th International Conference on Ubi-Media Computing (UMEDIA)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 8th International Conference on Ubi-Media Computing (UMEDIA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UMEDIA.2015.7297470\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 8th International Conference on Ubi-Media Computing (UMEDIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UMEDIA.2015.7297470","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Applying intuitionistic type-2 fuzzy inference system for e-learning system
This study applies an intuitionistic type-2 fuzzy inference system (FIS) to e-learning system. This study utilizes the advantages of intuitionistic fuzzy set and type-2 fuzzy inference system, which have been proven to be most effective in uncertain environment, to recommend e-learning courses. The learning readiness is difficultly found in e-leaning company. In the study, triangular fuzzy numbers (TFS) are used to represent uncertain data. Intuitionistic type-2 is employed to model vaguely defined relations between TFS. The numerical example is used to verify the intuitionistic type-2 can effectively infer the learning readiness, and recommend more objective e-learning courses than traditional fuzzy inference systems.