| dc.contributor.author |
Tutmez, B |
|
| dc.contributor.author |
Dag, A |
|
| dc.contributor.author |
Tercan, AE |
|
| dc.contributor.author |
Kaymak, U |
|
| dc.date.accessioned |
2022-10-19T11:24:48Z |
|
| dc.date.available |
2022-10-19T11:24:48Z |
|
| dc.date.issued |
2007 |
|
| dc.identifier.uri |
http://hdl.handle.net/11616/83084 |
|
| dc.description.abstract |
Thickness 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. |
|
| dc.description.abstract |
C1 [Tuetmez, B.] Inonu Univ, Dept Min Engn, Malatya, Turkey. |
|
| dc.description.abstract |
[Dag, A.] Cukurova Univ, Dept Min Engn, Adana, Turkey. |
|
| dc.description.abstract |
[Tercan, A. E.] Hacettepe Univ, Dept Min Engn, Ankara, Turkey. |
|
| dc.description.abstract |
[Kaymak, U.] Erasmus Univ, Inst Econometr, Rotterdam, Netherlands. |
|
| dc.source |
PROCEEDINGS OF THE 20TH INTERNATIONAL MINING CONGRESS AND EXHIBITION OF |
|
| dc.title |
Lignite thickness estimation via adaptive fuzzy-neural network |
|