Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems

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dc.authoridERKAYMAZ, OKAN/0000-0002-1996-8623
dc.contributor.authorErkaymaz, Okan
dc.contributor.authorOzer, Mahmut
dc.contributor.authorYumusak, Nejat
dc.date.accessioned2024-09-29T16:08:18Z
dc.date.available2024-09-29T16:08:18Z
dc.date.issued2014
dc.departmentKarabük Üniversitesien_US
dc.description.abstractSince feed-forward artificial neural networks (FFANNs) are the most widely used models to solve real-life problems, many studies have focused on improving their learning performances by changing the network architecture and learning algorithms. On the other hand, recently, small-world network topology has been shown to meet the characteristics of real-life problems. Therefore, in this study, instead of focusing on the performance of the conventional FFANNs, we investigated how real-life problems can be solved by a FFANN with small-world topology. Therefore, we considered 2 real-life problems: estimating the thermal performance of solar air collectors and predicting the modulus of rupture values of oriented strand boards. We used the FFANN with small-world topology to solve both problems and compared the results with those of a conventional FFANN with zero rewiring. In addition, we investigated whether there was statistically significant difference between the regular FFANN and small-world FFANN model. Our results show that there exists an optimal rewiring number within the small-world topology that warrants the best performance for both problems.en_US
dc.identifier.doi10.3906/elk-1202-89
dc.identifier.endpage718en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-84897858227en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage708en_US
dc.identifier.urihttps://doi.org/10.3906/elk-1202-89
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7480
dc.identifier.volume22en_US
dc.identifier.wosWOS:000332942900015en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSmall-world networken_US
dc.subjectfeed-forward artificial neural networken_US
dc.subjectrewiringen_US
dc.subjectnetwork topologyen_US
dc.titleImpact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problemsen_US
dc.typeArticleen_US

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