Please use this identifier to cite or link to this item: https://open.ni.ac.rs/handle/123456789/11
DC FieldValueLanguage
dc.contributor.authorMadić, Milošen
dc.contributor.authorMarkovic D.en
dc.contributor.authorRadovanović, Miroslaven
dc.date.accessioned2020-02-13T07:34:29Z-
dc.date.available2020-02-13T07:34:29Z-
dc.date.issued2012-12-01en
dc.identifier.issn17550386en
dc.identifier.urihttps://open.ni.ac.rs/handle/123456789/11-
dc.description.abstractThe application of artificial neural networks (ANNs) for modelling laser cutting is broad and ever increasing. The practical application of ANNs is mostly dependent on the success of the training process which is a complex task. Considering the disadvantages of backpropagation (BP) such as the convergence to local minima and slow convergence, this paper aims at investigating the possibilities of using novel meta-heuristic algorithms such as improved harmony search algorithm (IHSA) and cuckoo search algorithm (CSA) for training ANNs in modelling laser cutting. The validity and efficiency of the algorithms were verified by comparing the results with ANN model trained with real coded genetic algorithm (RCGA) which's superiority over BP has been well-documented. Statistical methods of the correlation coefficient and absolute percentage error indicate that the search space exploration capability of the IHSA and CSA are comparable to RCGA. It was shown that all three algorithms could be efficiently used for training of ANNs in modelling laser cutting. Copyright © 2012 Inderscience Enterprises Ltd.en
dc.relation.ispartofInternational Journal of Advanced Intelligence Paradigmsen
dc.titlePerformance comparison of meta-heuristic algorithms for training artificial neural networks in modelling laser cuttingen
dc.typeJournal/Magazine Articleen
dc.identifier.doi10.1504/IJAIP.2012.052073en
dc.identifier.scopus2-s2.0-84873911214en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/84873911214en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.relation.issue3-4en
dc.relation.volume4en
dc.relation.firstpage299en
dc.relation.lastpage312en
item.grantfulltextnone-
item.fulltextNo Fulltext-
crisitem.author.deptKatedra za proizvodno-informacione tehnologije-
crisitem.author.deptKatedra za proizvodno-informacione tehnologije-
crisitem.author.parentorgMašinski fakultet-
crisitem.author.parentorgMašinski fakultet-
Appears in Collections:Naučne i umetničke publikacije
Show simple item record

SCOPUSTM   
Citations

7
checked on Aug 3, 2020

Google ScholarTM

Check

Altmetric


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