Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6113
Title: A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing Application: Stock price prediction
Authors: Zarandi, Mohammad Hossein Fazel
Zarinbal, M.
Ghanbari, N.
Türkşen, İsmail Burhan
Keywords: Fuzzy functions
Noise-rejection possibilistic clustering
Multivariate adaptive regression splines
Simulated annealing
Forecasting
Issue Date: 2013
Publisher: Elsevier Science Inc
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.
URI: https://doi.org/10.1016/j.ins.2012.08.002
https://hdl.handle.net/20.500.11851/6113
ISSN: 0020-0255
1872-6291
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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