Optimization of Multilayer Perceptron Training Parameters Using Artificial Bee Colony and Genetic Algorithm

but.event.date23.04.2015cs
but.event.titleStudent EEICT 2015cs
dc.contributor.authorKartci, A.
dc.date.accessioned2015-08-25T08:43:02Z
dc.date.available2015-08-25T08:43:02Z
dc.date.issued2015cs
dc.description.abstractIn this paper, the momentum coefficient, learning rate, and the number of hidden neurons where the multilayer perceptron works best, are determined. The network and optimization algorithms are written in MATLAB, which was also successfully used to carry out results. To obtain the results, IRIS, mammographic_mass, and new_thyroid data sets have been used. Obtained results show that the determining effect on the neural learning process of parameters (momentum coefficient, learning rate, number of hidden neurons) are compatible with other approaches available in the literature. Both genetic algorithm (GA) and artificial bee colony (ABC) algorithm were successful on finding the values to get high performance as well as effect on performance of the population number.en
dc.formattextcs
dc.format.extent338-340cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings of the 21st Conference STUDENT EEICT 2015. s. 338-340. ISBN 978-80-214-5148-3cs
dc.identifier.isbn978-80-214-5148-3
dc.identifier.urihttp://hdl.handle.net/11012/43019
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 21st Conference STUDENT EEICT 2015en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectMultilayer perceptronen
dc.subjectartificial bee colony algorithmen
dc.subjectgenetic algorithmen
dc.subjecttraining parameters optimizationen
dc.titleOptimization of Multilayer Perceptron Training Parameters Using Artificial Bee Colony and Genetic Algorithmen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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