{"title":"The aggregation of information by examples via fuzzy sensors","authors":"G. Mauris, E. Benoit, L. Foulloy","doi":"10.1109/FUZZY.1994.343574","DOIUrl":null,"url":null,"abstract":"The problem of the aggregation of complementary information is a crucial point in the monitoring of large intelligent systems. The paper deals with the cases in which we do not have an analytical mathematic model to derive new information, nor a rule-based model at our disposal, but only a few examples expressed in a linguistic manner by basic fuzzy sensors. We explain within the frame of the fuzzy subset theory how the numeric-linguistic conversion is carried out inside a sensor, called a fuzzy sensor. Next, we present a method to extract rules from the examples studied, and how to obtain in this way a linguistic description of comfort from linguistic measurements of temperature and humidity. Finally, the advantages of our approach are pointed, out as well as the developments to come to improve the process of linguistic modelling from examples.<<ETX>>","PeriodicalId":153967,"journal":{"name":"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.1994.343574","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
The problem of the aggregation of complementary information is a crucial point in the monitoring of large intelligent systems. The paper deals with the cases in which we do not have an analytical mathematic model to derive new information, nor a rule-based model at our disposal, but only a few examples expressed in a linguistic manner by basic fuzzy sensors. We explain within the frame of the fuzzy subset theory how the numeric-linguistic conversion is carried out inside a sensor, called a fuzzy sensor. Next, we present a method to extract rules from the examples studied, and how to obtain in this way a linguistic description of comfort from linguistic measurements of temperature and humidity. Finally, the advantages of our approach are pointed, out as well as the developments to come to improve the process of linguistic modelling from examples.<>
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