Reliability modeling and analysis of mixture of exponential distributions using artificial neural network

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dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.authoridSindhu, Tabassum/0000-0001-9433-4981
dc.authoridLone, Showkat Ahmad/0000-0001-7149-3314
dc.authoridShafiq, Anum/0000-0001-7186-7216
dc.contributor.authorShafiq, Anum
dc.contributor.authorColak, Andac Batur
dc.contributor.authorLone, Showkat Ahmad
dc.contributor.authorSindhu, Tabassum Naz
dc.contributor.authorMuhammad, Taseer
dc.date.accessioned2024-11-07T13:34:53Z
dc.date.available2024-11-07T13:34:53Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn recent years, statisticians have become more and more interested in the study of mixture models, especially in the last decade, without adequately considering the difficulty of modeling the reliability measures of mixture models using artificial neural networks. In this study, in which artificial neural networks and mixed model reliability criteria are analyzed, various reliability parameters are calculated considering different scenarios. In order to estimate the obtained numerical reliability parameters, a multilayer artificial neural network model has been developed. Seven different reliability parameter values have been obtained from the artificial neural network model designed with four input parameters. The prediction values obtained from the artificial neural network model developed with five neurons in the hidden layer have been compared with numerical data, and the performance of the model has been analyzed comprehensively. The mean squared error (MSE) value for the network model has been calculated as 1.98E-08 and the R value as 0.99991. The results clearly revealed that the artificial neural network model developed using data from the appropriate statistical model is an excellent tool that can be used to estimate reliability measures.
dc.identifier.doi10.1002/mma.8178
dc.identifier.endpage3328
dc.identifier.issn0170-4214
dc.identifier.issn1099-1476
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85125854996
dc.identifier.scopusqualityQ1
dc.identifier.startpage3308
dc.identifier.urihttps://doi.org/10.1002/mma.8178
dc.identifier.urihttps://hdl.handle.net/11480/16223
dc.identifier.volume47
dc.identifier.wosWOS:000766010000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofMathematical Methods in the Applied Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectartificial neural network
dc.subjectmean inactivity time
dc.subjectmean residual life
dc.subjectmean time to failure
dc.subjectreliability function
dc.titleReliability modeling and analysis of mixture of exponential distributions using artificial neural network
dc.typeArticle

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