Aggregation Operations for Fusing Fuzzy Information

  • Ronald R. Yager
Part of the Computer Science Workbench book series (WORKBENCH)


Fuzzy sets can play an important role in the construction of human centered systems. They provide a mechanism for representing information in way that is compatible with human cognition. Particularly notable here is the idea of a linguistic variable [1]. In many of these applications we are faced with the problem of fusing or combining fuzzy subsets. For example, in multi-criteria decision making we need to combine the criteria to form an overall decision function. Since each criteria can be very naturally expressed as a fuzzy subset we are faced with the problem of combining fuzzy subsets. Fuzzy querying of data bases, information retrieval and pattern recognition also require us to face the problem of combining requirements expressed as fuzzy subsets. The construction of fuzzy systems models [2], which have been particularly successful in intelligent controllers, often require the aggregation of fuzzy subsets in the antecedents of the rules. Another class of problems which involves the fusion of fuzzy subsets arises in domains in which we obtain information from multiple sources and we desire to forge this information into one unified value. For example, if we search the Internet for information about the future exchange rate between the dollar and the yen we would get many estimates. Often these estimates are expressed in linguistic terms requiring the use of fuzzy subsets to translate them. Data mining [3] is another area where the fusion of fuzzy information is required.


Fuzzy Subset Aggregation Operator Fuzzy Measure Aggregation Operation Fuzzy Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  • Ronald R. Yager

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