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 |
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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