{"title":"基于离散2型模糊集的Takagi-Sugeno推理的两步模糊推理新方法","authors":"O. Uncu, I. Turksen","doi":"10.1109/NAFIPS.2003.1226751","DOIUrl":null,"url":null,"abstract":"Fuzzy system modeling (FSM) is one of the most prominent tools in order to capture the hidden behavior of highly nonlinear systems with uncertainty. In this paper, a new type 2 FSM approach is proposed in order to increase the predictive power of traditional Takagi-Sugeno fuzzy system models. One of the biggest problems of type 2 fuzzy system models is computational complexity. In order to remedy this problem, the proposed inference mechanism performs type reduction as a first step. Then, the type 1 inference mechanisms are utilized to deduce a model output for a given crisp observation.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A new two-step fuzzy inference approach based on Takagi-Sugeno inference using discrete type 2 fuzzy sets\",\"authors\":\"O. Uncu, I. Turksen\",\"doi\":\"10.1109/NAFIPS.2003.1226751\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fuzzy system modeling (FSM) is one of the most prominent tools in order to capture the hidden behavior of highly nonlinear systems with uncertainty. In this paper, a new type 2 FSM approach is proposed in order to increase the predictive power of traditional Takagi-Sugeno fuzzy system models. One of the biggest problems of type 2 fuzzy system models is computational complexity. In order to remedy this problem, the proposed inference mechanism performs type reduction as a first step. Then, the type 1 inference mechanisms are utilized to deduce a model output for a given crisp observation.\",\"PeriodicalId\":153530,\"journal\":{\"name\":\"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAFIPS.2003.1226751\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.2003.1226751","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new two-step fuzzy inference approach based on Takagi-Sugeno inference using discrete type 2 fuzzy sets
Fuzzy system modeling (FSM) is one of the most prominent tools in order to capture the hidden behavior of highly nonlinear systems with uncertainty. In this paper, a new type 2 FSM approach is proposed in order to increase the predictive power of traditional Takagi-Sugeno fuzzy system models. One of the biggest problems of type 2 fuzzy system models is computational complexity. In order to remedy this problem, the proposed inference mechanism performs type reduction as a first step. Then, the type 1 inference mechanisms are utilized to deduce a model output for a given crisp observation.
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