Lignite thickness estimation via adaptive fuzzy-neural network

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dc.authoridTERCAN, A.ERHAN/0000-0002-0393-4656
dc.authoridKaymak, Uzay/0000-0002-4500-9098
dc.authoridDag, Ahmet/0000-0003-4628-5067
dc.authoridTutmez, Bulent/0000-0002-2618-3285
dc.authorwosidTERCAN, A.ERHAN/G-5921-2013
dc.authorwosidTutmez, Bulent/ABG-8630-2020
dc.authorwosidKaymak, Uzay/A-3364-2008
dc.authorwosidDag, Ahmet/B-4046-2008
dc.contributor.authorTuetmez, B.
dc.contributor.authorDag, A.
dc.contributor.authorTercan, A. E.
dc.contributor.authorKaymak, U.
dc.date.accessioned2024-08-04T20:39:45Z
dc.date.available2024-08-04T20:39:45Z
dc.date.issued2007
dc.departmentİnönü Üniversitesien_US
dc.description20th International Mining Congress and Exhibition of Turkey (IMCET 2007) -- JUN 06-08, 2007 -- Ankara, TURKEYen_US
dc.description.abstractThickness estimation is an important step in reserve estimation. In this study, lignite thickness is estimated using fuzzy-neural network. For this purpose, the lignite thickness data derived from Afsin-Elbistan lignite deposit were employed and the estimation has been conducted by the Adaptive Network Based Fuzzy Inference System (ANFIS). The method estimates thickness based on a data-driven model structure which is constructed from the adaptation of artificial neural networks to fuzzy modelling algorithm. Modelling process consists of data clustering, inference and learning mechanisms. The results have been compared with kriging estimations and it is seen that performance of the model is high.en_US
dc.description.sponsorshipSci & Technol Res Council Turkey,Turkish Coal Enterprises,Eti Mine Works Gen Management,Turkish Hardcoal Enterprises,Yildizlar Holding,Black Sea Copper Works,Eti Copper Corp,Dedeman Holding,Chamber Mining Engineers Turkeyen_US
dc.identifier.endpage+en_US
dc.identifier.isbn978-9944-89-288-9
dc.identifier.scopus2-s2.0-84903154195en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage151en_US
dc.identifier.urihttps://hdl.handle.net/11616/96485
dc.identifier.wosWOS:000248292100018en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherUceat-Chamber Mining Engineers Turnkeyen_US
dc.relation.ispartofProceedings of The 20th International Mining Congress and Exhibition of Turkey, No 133en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject[No Keywords]en_US
dc.titleLignite thickness estimation via adaptive fuzzy-neural networken_US
dc.typeConference Objecten_US

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