Balanced Neurofuzzy Models

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dc.contributor.authorMytnyk, Oleg Yu.
dc.date.accessioned2025-05-12T08:00:06Z
dc.date.available2025-05-12T08:00:06Z
dc.date.issued2008
dc.description.abstractThis paper is devoted to the problem of a high complexity of fuzzy knowledge bases which contain enormous number of compound fuzzy rules. In order to significantly decrease the number of fuzzy rules and increase their transparency we present balanced neurofuzzy models. These models use the idea of Gabor-Kolmogorov expansion for additive decomposition into univariate and bivariate neurofuzzy submodels as well as maximum entropy principle to ground independent use of these submodels. Each submodel generates simplified rules independently of other submodels and contributes to fuzzy knowledge base of reduced complexity. The last but not least advantage of balanced neurofuzzy models is that they can be regularized and learned by modern inductive methods. Although the present paper omits learning. We demonstrate the potential of balanced neurofuzzy approach on a toy example of wind-induced wave model.
dc.format.pagerangeP. 148-152
dc.identifier.citationMytnyk, O. Yu. Balanced neurofuzzy models / Oleg Yu. Mytnyk // Proceedings of 2nd International Conference on Inductive Modelling, 15-19 Sept. 2008. - Kyiv : 2008. - P. 148-152. – Bibliogr.: 8 ref.
dc.identifier.orcid0009-0004-4706-0921
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/73755
dc.language.isoen
dc.publisher.placeKyiv
dc.relation.ispartofProceedings of the II International Conference on Inductive Modelling ICIM-2008, 15-19 September, 2008, Kyiv
dc.subjectfuzzy knowledge base (FKB)
dc.subjectneurofuzzy model
dc.subject.udc004.855
dc.titleBalanced Neurofuzzy Models
dc.typeArticle

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