Hybridization of the SGTM Neural-Like Structure Through Inputs Polynomial Extension

Basit Öğe Kaydı
dc.contributor.authorVitynskyi, P.
dc.contributor.authorTkachenko, R.
dc.contributor.authorIzonin, I.
dc.contributor.authorKutucu, H.
dc.date.accessioned2024-09-29T16:21:01Z
dc.date.available2024-09-29T16:21:01Z
dc.date.issued2018
dc.departmentKarabük Üniversitesien_US
dc.description2nd IEEE International Conference on Data Stream Mining and Processing, DSMP 2018 -- 21 August 2018 through 25 August 2018 -- Lviv -- 140524en_US
dc.description.abstractIn this paper, a new approach for increasing the approximation accuracy with the use of computational intelligence tools is described. It is based on the compatible use of the neural-like structure of the Successive Geometric Transformations Model and the inputs polynomial extension. To implement such an extension, second degree Wiener polynomial is used. This combination improves the method accuracy for solving various tasks, such as classification and regression, including short-term and long-term prediction, dynamic pricing, as well as image recognition and image scaling, e-commerce. Due to the use of SGTM neural-like structure, the high speed of the system is maintained in both training and using modes. The simulation of the described approach is carried out on real data, the time results of the neural-like structure work and the accuracy results (MAPE, RMSE, R) are given. A comparison of the operation of the method with existing ones, such as Support vector regression, Classic linear SGTM neural-like structure, Linear regression (using Stochastic Gradient Descent), Random Forest, Multilayer Perceptron, AdaBoost are made. The advantages of the developed approach, in particular with regard to the highest accuracy among existing ones were experimentally established. © 2018 IEEE.en_US
dc.identifier.doi10.1109/DSMP.2018.8478456
dc.identifier.endpage391en_US
dc.identifier.isbn978-153862874-4
dc.identifier.scopus2-s2.0-85056170780en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage386en_US
dc.identifier.urihttps://doi.org/10.1109/DSMP.2018.8478456
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9472
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP 2018en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectapproximationen_US
dc.subjectinput's extensionen_US
dc.subjectneural-like structuresen_US
dc.subjectSuccessive Geometric Transformation Modelen_US
dc.subjectWiener polynomialen_US
dc.titleHybridization of the SGTM Neural-Like Structure Through Inputs Polynomial Extensionen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu