Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/25467
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSayıl, Selahattin-
dc.contributor.authorLee, Kwangyun-
dc.date.accessioned2019-08-20T07:20:32Z
dc.date.available2019-08-20T07:20:32Z
dc.date.issued2002-
dc.identifier.issn2161-4393-
dc.identifier.urihttps://hdl.handle.net/11499/25467-
dc.identifier.urihttps://doi.org/10.1109/IJCNN.2002.1005463-
dc.description.abstractIn this paper, several possible algorithms and training methods for the CMAC network are analyzed thoroughly. Improvements are then examined and a Hybrid approach has been developed for the Maximum Error Algorithm by using the Neighborhood Training for the initial training period. The employment of the technique yielded faster initial convergence which is very Important for many control applications. The proposed hybrid approach is demonstrated in an inverse kinematics problem of a two-link robot arm.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofPROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURALen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCMAC algorithms; maximum error algorithm; neighborhood trainingen_US
dc.titleA hybrid maximum error algorithm with neighborhood training for CMACen_US
dc.typeConference Objecten_US
dc.identifier.startpage165
dc.identifier.startpage165en_US
dc.identifier.endpage170en_US
dc.identifier.doi10.1109/IJCNN.2002.1005463-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopus2-s2.0-0036078784en_US
dc.identifier.wosWOS:000177402800031en_US
dc.ownerPamukkale_University-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypeConference Object-
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

9
checked on Oct 13, 2024

WEB OF SCIENCETM
Citations

4
checked on Nov 16, 2024

Page view(s)

28
checked on Aug 24, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.