{"title":"用GWO算法优化Sugeno型模糊规则的非线性参数","authors":"M. Abdulgader, D. Kaur","doi":"10.1142/s1469026820500091","DOIUrl":null,"url":null,"abstract":"In this paper, a Sugeno type fuzzy system based on the fuzzy clustering has been developed for a variety of datasets. The number of rules for each dataset is based on the optimum number of clusters...","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing Nonlinear Parameters of Sugeno Type Fuzzy Rules using GWO for Data Classification\",\"authors\":\"M. Abdulgader, D. Kaur\",\"doi\":\"10.1142/s1469026820500091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a Sugeno type fuzzy system based on the fuzzy clustering has been developed for a variety of datasets. The number of rules for each dataset is based on the optimum number of clusters...\",\"PeriodicalId\":422521,\"journal\":{\"name\":\"Int. J. Comput. Intell. Appl.\",\"volume\":\"75 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Comput. Intell. Appl.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/s1469026820500091\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Comput. Intell. Appl.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s1469026820500091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimizing Nonlinear Parameters of Sugeno Type Fuzzy Rules using GWO for Data Classification
In this paper, a Sugeno type fuzzy system based on the fuzzy clustering has been developed for a variety of datasets. The number of rules for each dataset is based on the optimum number of clusters...
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