Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/6113
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zarandi, Mohammad Hossein Fazel | - |
dc.contributor.author | Zarinbal, M. | - |
dc.contributor.author | Ghanbari, N. | - |
dc.contributor.author | Türkşen, İsmail Burhan | - |
dc.date.accessioned | 2021-09-11T15:34:58Z | - |
dc.date.available | 2021-09-11T15:34:58Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.issn | 1872-6291 | - |
dc.identifier.uri | https://doi.org/10.1016/j.ins.2012.08.002 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6113 | - |
dc.description.abstract | In this paper, a new fuzzy functions (FFs) model is presented and its main parameters are optimized with simulated annealing (SA) approach. For this purpose, a new hybrid clustering algorithm for model structure identification is proposed. This model is based on hybridization of extended version of possibilistic c-mean (PCM) clustering with mahalonobise distance measure and a noise rejection method. In this research, Multivariate Adaptive Regression Splines (MARS) is applied for selecting variables and approximating fuzzy functions in each cluster. A metaheuristic Imperialist Competitive Algorithm (ICA) is used to initialize the clustering parameters. The proposed FFs model is validated using two well-known standard artificial datasets and two real datasets, Tehran stock exchange and ozone level. It is shown that using the proposed FFs model can lead to promising results. (C) 2012 Elsevier Inc. All rights reserved. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Science Inc | en_US |
dc.relation.ispartof | Information Sciences | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Fuzzy functions | en_US |
dc.subject | Noise-rejection possibilistic clustering | en_US |
dc.subject | Multivariate adaptive regression splines | en_US |
dc.subject | Simulated annealing | en_US |
dc.subject | Forecasting | en_US |
dc.title | A New Fuzzy Functions Model Tuned by Hybridizing Imperialist Competitive Algorithm and Simulated Annealing Application: Stock Price Prediction | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Industrial Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 222 | en_US |
dc.identifier.startpage | 213 | en_US |
dc.identifier.endpage | 228 | en_US |
dc.identifier.wos | WOS:000313774200015 | en_US |
dc.identifier.scopus | 2-s2.0-84870062990 | en_US |
dc.institutionauthor | Türkşen, İsmail Burhan | - |
dc.identifier.doi | 10.1016/j.ins.2012.08.002 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q1 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | Endüstri Mühendisliği Bölümü / Department of Industrial Engineering Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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