Monotone data samples do not always produce monotone fuzzy if-then rules: Learning with ad hoc and system identification methods
Version 2 2024-06-06, 08:08
Version 1 2023-02-28, 02:22
conference contribution
posted on 2024-06-06, 08:08 authored by Y Teh, KM Tay, Chee Peng LimMonotone data samples do not always produce monotone fuzzy if-then rules: Learning with ad hoc and system identification methods
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Location
Naples, ItalyLanguage
EnglishPublication classification
E1 Full written paper - refereedPagination
1-7Start date
2017-07-09End date
2017-07-12ISSN
1098-7584ISBN-13
9781509060344Title of proceedings
FUZZ-IEEE 2017 : IEEE International Conference on Fuzzy SystemsEvent
IEEE International Conference on Fuzzy Systems (2017 : Naples, Italy)Publisher
IEEEPlace of publication
Piscataway, N.J.Series
IUsage metrics
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No categories selectedKeywords
Computer ScienceComputer Science, Artificial IntelligenceComputer Science, Theory & MethodsEngineeringEngineering, Electrical & ElectronicFuzzy If-Then rulesLOGIC SYSTEMSmachine learningMODELSmonotone fuzzy rule relabelingmonotonicitymulti-attribute monotone dataScience & TechnologyTechnologyTSK Fuzzy inference system
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