Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/9824
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kukolj, Dragan | en |
dc.contributor.author | Berko-Pusic M. | en |
dc.contributor.author | Atlagić, Branislav | en |
dc.date.accessioned | 2020-03-03T14:35:09Z | - |
dc.date.available | 2020-03-03T14:35:09Z | - |
dc.date.issued | 2001-11-01 | en |
dc.identifier.issn | 8900604 | en |
dc.identifier.uri | https://open.uns.ac.rs/handle/123456789/9824 | - |
dc.description.abstract | This article presents the results of research concerning possibilities of applying multilayer perceptron type of neural network for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the multilayer perceptron was considered. The emphasis was put on the multilayer perceptrons' function as a state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of multilayer perceptrons were analyzed. | en |
dc.relation.ispartof | Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM | en |
dc.title | Experimental design of supervisory control functions based on multilayer perceptrons | en |
dc.type | Journal/Magazine Article | en |
dc.identifier.scopus | 2-s2.0-0035519061 | en |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/0035519061 | en |
dc.relation.lastpage | 431 | en |
dc.relation.firstpage | 425 | en |
dc.relation.issue | 5 | en |
dc.relation.volume | 15 | en |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Fakultet tehničkih nauka, Departman za računarstvo i automatiku | - |
crisitem.author.parentorg | Fakultet tehničkih nauka | - |
Appears in Collections: | FTN Publikacije/Publications |
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