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https://hdl.handle.net/20.500.11851/7786
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Efe, Mehmet Önder | - |
dc.date.accessioned | 2021-09-11T15:59:47Z | - |
dc.date.available | 2021-09-11T15:59:47Z | - |
dc.date.issued | 2006 | en_US |
dc.identifier.citation | 16th International Conference on Artificial Neural Networks (ICANN 2006) -- SEP 10-14, 2006 -- Athens, GREECE | en_US |
dc.identifier.isbn | 3-540-38625-4 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/7786 | - |
dc.description.abstract | Compact representation of knowledge having strong internal interactions has become possible with the developments in neurocomputing and neural information processing. The field of neural networks has offered various solutions for complex problems, however, the problems associated with the learning performance has constituted a major drawback in terms of the realization performance and computational requirements. This paper discusses the use of variable structure systems theory in learning process. The objective is to incorporate the robustness of the approach into the training dynamics, and to ensure the stability in the adjustable parameter space. The results discussed demonstrate the fulfillment of the design specifications and display how the strength of a robust control scheme could be an integral part of a learning system. This paper discusses how Gaussian radial basis function neural networks could be utilized to drive a mechatronic system's behavior into a predefined sliding regime, and it is seen that the results are promising. | en_US |
dc.description.sponsorship | European Neural Network Soc, Int Neural Network Soc, Japanese Neural Network Soc, IEEE Computat Intelligence Soc | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer-Verlag Berlin | en_US |
dc.relation.ispartof | Artificial Neural Networks - Icann 2006, Pt 1 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Gaussian radial basis function networks | en_US |
dc.subject | sliding mode control | en_US |
dc.title | VSC perspective for neurocontroller tuning | en_US |
dc.type | Conference Object | en_US |
dc.relation.ispartofseries | Lecture Notes in Computer Science | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Electrical and Electronics Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 4131 | en_US |
dc.identifier.startpage | 918 | en_US |
dc.identifier.endpage | 927 | en_US |
dc.authorid | 0000-0002-5992-895X | - |
dc.identifier.wos | WOS:000241472100095 | en_US |
dc.identifier.scopus | 2-s2.0-33749870248 | en_US |
dc.institutionauthor | Önder Efe, Mehmet | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.relation.conference | 16th International Conference on Artificial Neural Networks (ICANN 2006) | en_US |
dc.identifier.scopusquality | Q2 | - |
item.openairetype | Conference Object | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.5. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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