Show simple item record

dc.contributor.authorKolodyazhniy, Vitaliy
dc.contributor.authorOtto, Peter
dc.contributor.editorJantke, Klaus P.
dc.contributor.editorFähnrich, Klaus-Peter
dc.contributor.editorWittig, Wolfgang S.
dc.date.accessioned2019-08-27T08:15:08Z
dc.date.available2019-08-27T08:15:08Z
dc.date.issued2030
dc.identifier.isbn3-88579-401-27
dc.identifier.issn1617-5493
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/24898
dc.description.abstractIn the paper, a novel Neuro-Fuzzy Kolmogorov's Network (NFKN) is considered. The NFKN is based on the famous Kolmogorov’s superposition theorem (KST). The network consists of two layers of neo-fuzzy neurons (NFNs) and is linear in both the hidden and output layer parameters, so it can be trained with very fast and simple procedures without any nonlinear operations. The validity of theoretical results and the advantages of the NFKN are confirmed by application examples: electric load forecasting, and classification of data from medical and banking domains.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e. V.
dc.relation.ispartofMarktplatz Internet: Von e-Learning bis e-Payment, 13. Leipziger Informatik-Tage (LIT 2005)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-96
dc.titleNeuro-Fuzzy Modelling Based on Kolmogorov's Superposition: a New Tool for Prediction and Classificationen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages273-280
mci.conference.sessiontitleRegular Research Papers
mci.conference.locationLeipzig
mci.conference.date21.-23. September 2030


Files in this item

Thumbnail

Show simple item record