Hybrid neural network and genetic algorithm based machining feature recognition

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dc.contributor.buuauthorÖztürk, Nursel
dc.contributor.buuauthorÖztürk, Ferruh
dc.contributor.departmentUludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.tr_TR
dc.contributor.departmentUludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.tr_TR
dc.contributor.researcheridAAG-9336-2021tr_TR
dc.contributor.researcheridAAG-9923-2021tr_TR
dc.contributor.scopusid7005688805tr_TR
dc.contributor.scopusid56271685800tr_TR
dc.date.accessioned2021-10-07T11:27:01Z
dc.date.available2021-10-07T11:27:01Z
dc.date.issued2004-06
dc.description.abstractIn this research, neural networks (NNs) and genetic algorithms (GAs) are used together in a hybrid approach to reduce the computational complexity of feature recognition problem. The proposed approach combines the characteristics of evolutionary technique and NN to overcome the shortcomings of feature recognition problem. Consideration is given to reduce the computational complexity of network with specific interest to design the optimum network architecture using GA input selection approach. In order to evaluate the performance of the proposed system, experimental results are compared with previous NN based feature recognition research.en_US
dc.identifier.citationÖztürk, N. ve Öztürk, F. (2004). “Hybrid neural network and genetic algorithm based machining feature recognition”. Journal of Intelligent Manufacturing, 15(3), 287-298.en_US
dc.identifier.endpage298tr_TR
dc.identifier.issn0956-5515
dc.identifier.issue3tr_TR
dc.identifier.scopus2-s2.0-3543131353tr_TR
dc.identifier.startpage287tr_TR
dc.identifier.urihttps://doi.org/10.1023/B:JIMS.0000026567.63397.d5
dc.identifier.urihttps://link.springer.com/article/10.1023/B:JIMS.0000026567.63397.d5
dc.identifier.urihttp://hdl.handle.net/11452/22281
dc.identifier.volume15tr_TR
dc.identifier.wos000221206200002tr_TR
dc.indexed.scopusScopusen_US
dc.indexed.wosSCIEen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.journalJournal of Intelligent Manufacturingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputer scienceen_US
dc.subjectEngineeringen_US
dc.subjectFeature recognitionen_US
dc.subjectNeural networksen_US
dc.subjectGenetic input selectionen_US
dc.subjectManufacturing featuresen_US
dc.subjectDesignen_US
dc.subjectClassificationen_US
dc.subjectSystemen_US
dc.subjectSearchen_US
dc.subjectModelen_US
dc.subjectBackpropagationen_US
dc.subjectComputational complexityen_US
dc.subjectComputer aided manufacturingen_US
dc.subjectFeature extractionen_US
dc.subjectGenetic algorithmsen_US
dc.subjectImage processingen_US
dc.subjectMachiningen_US
dc.subjectMathematical modelsen_US
dc.subjectParameter estimationen_US
dc.subjectProblem solvingen_US
dc.subjectComputer aided production systemsen_US
dc.subjectFeature recognitionen_US
dc.subjectGenetic input selectionen_US
dc.subjectNetwork modelen_US
dc.subjectNeural networksen_US
dc.subject.scopusComputer Aided Process Planning; Feature Recognition; Machiningen_US
dc.subject.wosComputer science, artificial intelligenceen_US
dc.subject.wosEngineering, manufacturingen_US
dc.titleHybrid neural network and genetic algorithm based machining feature recognitionen_US
dc.typeArticle
dc.wos.quartileQ3 (Computer science, artificial intelligence)en_US
dc.wos.quartileQ2 (Engineering, manufacturing)en_US

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