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Data-Based Technique for Extracting Knowledge from Data Generated in Experiments

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dc.contributor.author ZAPOROJAN, Sergiu
dc.contributor.author CARBUNE, Viorel
dc.contributor.author CALMICOV, Igori
dc.date.accessioned 2021-10-12T09:02:28Z
dc.date.available 2021-10-12T09:02:28Z
dc.date.issued 2020
dc.identifier.citation ZAPOROJAN, Sergiu, CARBUNE Viorel, CALMICOV, Igori. Data-Based Technique for Extracting Knowledge from Data Generated in Experiments. In: 16th International Conference on Intelligent Computer Communication and Processing: proc. 16th IEEE ICCP, 3-5 Sept. 2020, Cluj-Napoca, Romania, 2020, pp13-19. ISBN 978-1-7281-9080-8. en_US
dc.identifier.isbn 978-1-7281-9080-8
dc.identifier.uri https://doi.org/10.1109/ICCP51029.2020.9266187
dc.identifier.uri http://repository.utm.md/handle/5014/17687
dc.description Access full text - https://doi.org/10.1109/ICCP51029.2020.9266187 en_US
dc.description.abstract Fuzzy sets are used in different fields and determination of the membership functions is one of the most important issues in the design of fuzzy systems. The paper presents an approach to that problem to provide solutions in specific cases. In context, a technique for extracting knowledge from measurements data sets was developed that allows to retrieve human expertise and the construction of algorithms for decision-making systems. Initially, the method was developed to be used in data-based fuzzy modeling for the micro-wire casting plant control. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject experiments data en_US
dc.subject fuzzy systems en_US
dc.subject measurements data en_US
dc.subject decision-making systems en_US
dc.title Data-Based Technique for Extracting Knowledge from Data Generated in Experiments en_US
dc.type Article en_US


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