Interpolativity of at-least and at-most models of monotone fuzzy rule bases with multiple antecedent variables

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F16%3AA17017C6" target="_blank" >RIV/61988987:17610/16:A17017C6 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Interpolativity of at-least and at-most models of monotone fuzzy rule bases with multiple antecedent variables
Original language description
Among the many desirable properties of fuzzy inference systems not all of them are known to co-exist. For instance, the defuzzified output obtained from a system based on a monotone fuzzy rule base need not be monotonic and interpolative simultaneously. Recently, ˇStˇepniˇcka and De Baets have investigated and shown the co-existence of the above two properties in the case of fuzzy relational inference systems and single-input-single-output (SISO) rule bases. An extension of these results to the multiple-input-singleoutput (MISO) case is not straight-forward owing to the lack of a natural ordering in higher dimensions. In this work, we study the MISO case and show that similar results are available when the monotone rule base is modeled based on at-most and at-least modifiers.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BA - General mathematics
OECD FORD branch
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Project
<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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