{"title":"Plıthogenıc Combıned Dısjoınt Block Fuzzy Cognıtıve Maps (PCDBFCM)","authors":"D. D.Nagarajan, A. Rajkumar","doi":"10.54216/jnfs.040203","DOIUrl":null,"url":null,"abstract":"This research article represents an innovative concept in Plithogenic Combined Disjoint Block Fuzzy Cognitive Maps (PCDBFCM) and its applications. PCDBFCM is a very useful tool in grouping the factors with contradiction degree of multiple attributes. A plithogenic fuzzy matrix is used to represent the connection matrix. The resultant vector is obtained while using plithogenic fuzzy operators. The produced results are very useful in making decisions since they include the degree of conceptual node contradiction with respect to the dominant node. For the plithogenic aggregation operators, the degree of dissimilarity between each attribute value and the main attribute value of the attribute leads to increased accuracy.","PeriodicalId":438286,"journal":{"name":"Journal of Neutrosophic and Fuzzy Systems","volume":"17 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neutrosophic and Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54216/jnfs.040203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This research article represents an innovative concept in Plithogenic Combined Disjoint Block Fuzzy Cognitive Maps (PCDBFCM) and its applications. PCDBFCM is a very useful tool in grouping the factors with contradiction degree of multiple attributes. A plithogenic fuzzy matrix is used to represent the connection matrix. The resultant vector is obtained while using plithogenic fuzzy operators. The produced results are very useful in making decisions since they include the degree of conceptual node contradiction with respect to the dominant node. For the plithogenic aggregation operators, the degree of dissimilarity between each attribute value and the main attribute value of the attribute leads to increased accuracy.