dc.contributor.author |
Georgilakis, P |
en |
dc.contributor.author |
Amoiralis, E |
en |
dc.date.accessioned |
2014-03-01T01:56:10Z |
|
dc.date.available |
2014-03-01T01:56:10Z |
|
dc.date.issued |
2007 |
en |
dc.identifier.uri |
https://dspace.lib.ntua.gr/xmlui/handle/123456789/27987 |
|
dc.subject |
Artificial Intelligent |
en |
dc.subject |
Optimum Design |
en |
dc.subject |
Decision Tree |
en |
dc.subject |
Neural Network |
en |
dc.title |
Spotlight on transformer design |
en |
heal.type |
journalArticle |
en |
heal.identifier.primary |
10.1109/MPAE.2007.264851 |
en |
heal.identifier.secondary |
http://dx.doi.org/10.1109/MPAE.2007.264851 |
en |
heal.publicationDate |
2007 |
en |
heal.abstract |
This paper presents an integrated artificial intelligence technique to achieve an optimum design of a transformer. AI is used to reach an optimum transformer design solution for the winding material selection problem. To be more precise, decision trees (DTs) and adaptive trained neural networks (ATNNs) are combined with the aim of selecting the appropriate winding material (Cu or Al) to |
en |
heal.journalName |
IEEE Power & Energy Magazine |
en |
dc.identifier.doi |
10.1109/MPAE.2007.264851 |
en |