{"title":"用模糊玫瑰图可视化不确定性","authors":"A. Buck, J. Keller","doi":"10.1109/CIES.2014.7011827","DOIUrl":null,"url":null,"abstract":"This paper presents a novel method for visualizing vectors of fuzzy numbers. The proposed approach is an extension of the standard polar area diagram and can be applied to a single uncertain vector or a fuzzy weighted graph with vectors of fuzzy attributes on the vertices and/or edges. The resulting diagrams are intuitive to understand and do not require an extensive background in fuzzy set theory. By visualizing uncertain vectors in this way, the viewer can easily compare and contrast sets of fuzzy numbers. This can be useful in the context of decision support systems, particularly those involving multi-criteria decision making. We demonstrate our approach on the problem of finding a least-cost path through an uncertain environment.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Visualizing uncertainty with fuzzy rose diagrams\",\"authors\":\"A. Buck, J. Keller\",\"doi\":\"10.1109/CIES.2014.7011827\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel method for visualizing vectors of fuzzy numbers. The proposed approach is an extension of the standard polar area diagram and can be applied to a single uncertain vector or a fuzzy weighted graph with vectors of fuzzy attributes on the vertices and/or edges. The resulting diagrams are intuitive to understand and do not require an extensive background in fuzzy set theory. By visualizing uncertain vectors in this way, the viewer can easily compare and contrast sets of fuzzy numbers. This can be useful in the context of decision support systems, particularly those involving multi-criteria decision making. We demonstrate our approach on the problem of finding a least-cost path through an uncertain environment.\",\"PeriodicalId\":287779,\"journal\":{\"name\":\"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIES.2014.7011827\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIES.2014.7011827","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a novel method for visualizing vectors of fuzzy numbers. The proposed approach is an extension of the standard polar area diagram and can be applied to a single uncertain vector or a fuzzy weighted graph with vectors of fuzzy attributes on the vertices and/or edges. The resulting diagrams are intuitive to understand and do not require an extensive background in fuzzy set theory. By visualizing uncertain vectors in this way, the viewer can easily compare and contrast sets of fuzzy numbers. This can be useful in the context of decision support systems, particularly those involving multi-criteria decision making. We demonstrate our approach on the problem of finding a least-cost path through an uncertain environment.