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Unsupervised constrained neural network modeling of boundary value corneal model for eye surgery

dc.contributor.authorUmar, Muhammad
dc.contributor.authorAmin, Fazli
dc.contributor.authorWahab, Hafiz Abdul
dc.contributor.authorBaleanu, Dumitru
dc.contributor.authorID56389tr_TR
dc.date.accessioned2021-02-16T12:43:28Z
dc.date.available2021-02-16T12:43:28Z
dc.date.issued2019
dc.departmentÇankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractIn this article, a numerical computing technique is developed for solving the nonlinear second order corneal shape model (CSM) using feed-forward artificial neural networks, optimized with particle swarm optimization (PSO) and active-set algorithms (ASA). The design parameter is approved initially with PSO known as global search, while for further prompt local refinements ASA is used. The performance of the design structure is scrutinized by solving a number of variants of CSM. The typical Adams numerical results are used for comparison of the proposed results, which establish the worth of the scheme in terms of convergence and accuracy. For more satisfaction, the present results are also compared with radial basis function (RBF) results. Moreover, statistical analysis based on mean absolute deviation, Theil's inequality coefficient and Nash Sutcliffe efficiency is presented (C) 2019 Published by Elsevier B.V.en_US
dc.description.publishedMonth12
dc.identifier.citationUmar, Muhammad...et al. (2019). "Unsupervised constrained neural network modeling of boundary value corneal model for eye surgery", Applied Soft Computing, Vol. 85.en_US
dc.identifier.doi10.1016/j.asoc.2019.105826
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.urihttp://hdl.handle.net/20.500.12416/4590
dc.identifier.volume85en_US
dc.language.isoenen_US
dc.relation.ispartofApplied Soft Computingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNonlinearen_US
dc.subjectCorneal Shape Modelen_US
dc.subjectArtificial Neural Networken_US
dc.subjectStatistical Analysisen_US
dc.subjectActive-Seten_US
dc.subjectParticle Swarm Optimizationen_US
dc.titleUnsupervised constrained neural network modeling of boundary value corneal model for eye surgerytr_TR
dc.titleUnsupervised Constrained Neural Network Modeling of Boundary Value Corneal Model for Eye Surgeryen_US
dc.typeArticleen_US
dspace.entity.typePublication

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