Model reduction of a large scale system using PCA technique

Basit Öğe Kaydı
dc.contributor.authorKouadri, Abdelmalek
dc.contributor.authorZelmat, Mimoun
dc.contributor.authorNamoune, Abdallah
dc.date.accessioned2024-07-12T20:51:30Z
dc.date.available2024-07-12T20:51:30Z
dc.date.issued2009en_US
dc.departmentFakülteler, İnsan ve Toplum Bilimleri Fakültesi, Matematik Bölümüen_US
dc.description.abstractMost of the model reduction techniques proposed in the literature are based on the use of multivariate statistical techniques. The linear Principal Component Analysis (PCA) is one of the most known methods in data analysis. It looks for one subspace of a smaller dimension than the initial space and projects the studied data into this space with a minimum loss of information. Therefore, the obtained result is a representation of data with a reduction of dimension. To reduce calculations, in the case where the correlation matrix is large, the neural network of the PCA has been proposed. In general, neural network approaches in PCA distinguish themselves through two criteria of optimised training that are equivalent: variances maximization of data projection and quadratic error minimization of estimated data. Most approaches which use networks of multi-layer perceptron for obtaining the non-linear PCA model (NLPCA) encounter problems of optimization often non-linear such as the headache of convergence and initialization of this network type. For this reason, while combining the main curves and the Radial Basis Function Neural Networks, we propose an approach for the NLPCA with two networks of three cascading layers. The problem of training presents a linear regression in relation with the output layer weights. The algorithm which determines the number of nonlinear components to be retained in the NLPCA model is based on the accumulate variance.en_US
dc.identifier.citationKouadri, A., Zelmat, M. ve Namoune, A. (2009). Model reduction of a large scale system using PCA technique. Maltepe Üniversitesi, İnsan ve Toplum Bilimleri Fakültesi. s. 62.en_US
dc.identifier.endpage63en_US
dc.identifier.isbn9.78605E+12
dc.identifier.startpage62en_US
dc.identifier.urihttps://www.maltepe.edu.tr/Content/Media/CkEditor/03012019014112056-AbstractBookICMS2009Istanbul.pdf#page=76
dc.identifier.urihttps://hdl.handle.net/20.500.12415/2423
dc.language.isoenen_US
dc.publisherMaltepe Üniversitesien_US
dc.relation.ispartofInternational Conference of Mathematical Sciencesen_US
dc.relation.publicationcategoryUluslararası Konferans Öğesi - Başka Kurum Yazarıen_US
dc.rightsCC0 1.0 Universal*
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.snmzKY07788
dc.titleModel reduction of a large scale system using PCA techniqueen_US
dc.typeConference Object
dspace.entity.typePublication

Dosyalar

Koleksiyon

İnsan ve Toplum Bilimleri Fakültesi Koleksiyonu