Using Simulation to Compare Two Methods of Estimating the Fuzzy Linear Regression Model
DOI:
https://doi.org/10.31272/jae.i143.1220Keywords:
A.R.Arabpour and M.Tata method, , Fuzzy linear regression model, Mean square error, ST-decomposition method, SimulationAbstract
Fuzzy linear regression is the optimal tool for analyzing data that is not accurate or clearly defined; using fuzzy tools in analyzing such data can improve the accuracy and reliability of estimating relationships between variables. This research deals with a comparison between two methods for assessing the fuzzy linear regression model with a fuzzy triangular response variable and crisp (non-fuzzy) explanatory variables and fuzzy triangular parameters (A.R.Arabpour and M.Tata method for estimation, the ST-decomposition method for analysis). The comparison was based on the Mean Square Error (MSE) measurement; simulation was used with five sample sizes of (25,50,100,150,200), mean values of (0,2,4), and variance values of (0.1,0.5,0.9), with 1000 repetitions for each experiment. The MATLAB program was used to obtain the results; through comparison, it was found that the A.R.Arabpour And M.Tata method is the best because it has the lowest MSE value. Therefore, efficient estimators capable of representing the fuzzy model can be obtained
References
خضر، سليمة محمد، (2022م) "الاعداد الضبابية" ،بحث منشور، مجلة التربوي، العدد 20.
الطائي، فاضلة علي جيحان، (2007م) "الضبابية في البرمجة الخطية مع تطبيق عملي" ، رسالة ماجستير مقدمة الى كلية الادارة والاقتصاد، الجامعة المستنصرية.
عباس، مروان صبري، (2021م) "مقارنة تطبيقية بين نماذج الانحدار الضبابي" ، رسالة ماجستير مقدمة الى كلية الادارة والاقتصاد، الجامعة المستنصرية.
فرحان، علي محمد، (2013م) "بناء نموذج انحدار خطي متعدد ضبابي لأسعار النفط العالمية" ، رسالة ماجستير، كلية الادارة والاقتصاد، جامعة بغداد.
هندوش، رنا وليد بهنام، (2009م) "تطبيق المنطق المضبب لنمذجة الكثافة الانتاجية لمعمل الالبسة الولادي" ،بحث منشور، المجلة العراقية للعلوم الاحصائية.
Arabpour, A. R., & Tata, M. (2008). "Estimating the parameters of a fuzzy linear regression model". Iranian Journal of Fuzzy Systems, 5(2), 1-19.
Friedman, M., Ming, M., & Kandel, A. (1998)." Fuzzy linear systems". Fuzzy sets and systems, 96(2), 201-209.
González-Rodríguez, G., Colubi, A., & Gil, M. A. & Coppi, R. (2006). "A Method to Simulate Fuzzy Random Variables". Soft Methods for Integrated Uncertainty Modelling (pp.103-110) Chapter: A Method to Simulate Fuzzy Random Variables.
Jafarian, A. (2016)." New decomposition method for solving dual fully fuzzy linear systems". International Journal of Fuzzy Computation and Modelling, 2(1), 76-85.
Klir, G. J., & Yuan, B. (1996). "Fuzzy sets and fuzzy logic: theory and applications". Possibility Theory versus Probab. Theory, 32(2), 207-208.
Nowakov´a, J.& Pokorn´y, M. (2013). "Fuzzy Linear Regression Analysis." 12th IFAC Conference on Programmable Devices and Embedded Systems, Czech Republic.
Pedrycz, W., & Gomide, F. (1998)." An introduction to fuzzy sets: analysis and design". MIT Press.
Ramly, N., Rusiman, M. S., Ismail, S., Hamzah, F. M., & Gürünlü Alma, Ö. (2023)." An adjustment degree of fitting on fuzzy linear regression model toward manufacturing income".
Shapiro, A. F. (2005)." Fuzzy regression models". Article of Penn State University, 102(2), 373-383.
Vijayalakshmi, V., & Sattanathan, R. (2011)." ST decomposition method for solving fully fuzzy linear systems using Gauss Jordan for trapezoidal fuzzy matrices". In International Mathematical Forum (Vol. 6, No. 45, pp. 2245-2254).
Wu, H. C. (2003)." Linear regression analysis for fuzzy input and output data using the extension principle". Computers & Mathematics with Applications, 45(12), 1849-1859.
Zadeh, L. A. (1965)." Fuzzy sets". Information and control, 8(3), 338-353.
Zareamoghaddam, H., & Zareamoghaddam, Z. (2014). "A new algorithm for fuzzy linear regression with crisp inputs and fuzzy output". International Journal of Nonlinear Science, 17(2), 128-134.
Downloads
Published
Issue
Section
License
The journal of Administration & Economics is an open- access journal that all contents are free of charge. Articles of this journal are licensed under the terms of the Creative Commons Attribution International Public License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode) that licensees are unrestrictly allowedto search, download, share, distribute, print, or link to the full text of the articles, crawl them for indexing and reproduce any medium of the articles provided that they give the author(s) proper credits (citation). The journal allows the author(s) to retain the copyright of their published article.
Creative Commons-Attribution (BY) 
