Publication:
Statistical and computational intelligence tools for the analyses of warp tension in different back-rest oscillations

dc.contributor.authorTurhan, Yıldıray
dc.contributor.authorTokat, Sezai
dc.contributor.buuauthorEren, Recep
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentTekstil Mühendisliği Bölümü
dc.contributor.scopusid8649952300
dc.date.accessioned2022-10-31T08:32:41Z
dc.date.available2022-10-31T08:32:41Z
dc.date.issued2007-12-01
dc.description.abstractIn this paper, experimental, computational intelligence based and statistical investigations of warp tensions in different back-rest oscillations are presented. Firstly, in the experimental stage, springs having different stiffnesses are used to obtain different back-rest oscillations. For each spring, fabrics are woven in different weft densities and the warp tensions are measured and saved during weaving process. Secondly, in the statistical investigation, the experimental data are analyzed by using linear multiple and quadratic multiple-regression models. Later, in the computational intelligence based investigation, the data obtained from the experimental study are analyzed by using artificial neural networks that are universal approximators which provide a massively parallel processing and decentralized computing. Specialty, radial basis function neural network structure is chosen. In this structure, cross-validation technique is used in order to determine the number of radial basis functions. Finally, the results of regression analysis, the computational intelligence based analysis and experimental measurements are compared by using the coefficient of determination. From the results, it is shown that the computational intelligence based analysis indicates a better agreement with the experimental measurement than the statistical analysis.
dc.identifier.citationTurhan, Y. vd. (2007). "Statistical and computational intelligence tools for the analyses of warp tension in different back-rest oscillations". Information Sciences, 177(23), 5237-5252.
dc.identifier.endpage5252
dc.identifier.issn0020-0255
dc.identifier.issn1872-6291
dc.identifier.issue23
dc.identifier.scopus2-s2.0-34548604302
dc.identifier.startpage5237
dc.identifier.urihttps://doi.org/10.1016/j.ins.2007.06.029
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0020025507003246
dc.identifier.urihttp://hdl.handle.net/11452/29271
dc.identifier.volume177
dc.identifier.wos000250285400009
dc.indexed.wosSCIE
dc.language.isoen
dc.publisherElsevier Science
dc.relation.collaborationYurt içi
dc.relation.journalInformation Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBack-rest oscillation
dc.subjectQuadratic programming
dc.subjectWarp tension
dc.subjectNeural networks
dc.subjectBack-rest oscillations
dc.subjectCross-validation technique
dc.subjectFabrics
dc.subjectData regression
dc.subjectMathematical models
dc.subjectMultiple-regression models
dc.subjectWeft densities
dc.subjectParallel processing systems
dc.subjectRadial basis function networks
dc.subjectRegression analysis
dc.subjectStiffness
dc.subjectWeaving
dc.subjectCross-validation
dc.subjectData regression
dc.subjectNeural networks
dc.subjectRadial basis function
dc.subjectWarp tension
dc.subjectWeft density
dc.subjectPerformance
dc.subjectRBF
dc.subjectComputer science
dc.subject.scopusAir-Jet Loom; Weft Insertion; Yarn Tension
dc.subject.wosComputer science, information systems
dc.titleStatistical and computational intelligence tools for the analyses of warp tension in different back-rest oscillations
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Tekstil Mühendisliği Bölümü
local.indexed.atScopus
local.indexed.atWOS

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