Estimation of the fuzzy linear regression model using the method of A.R. Arabpour And M.Tata
DOI:
https://doi.org/10.31272/jae.i144.1243Keywords:
A Fuzzy regression model, Membership Function, Fuzzy data, t-test for the significance of fuzzy parametersAbstract
Fuzzy Linear Regression is the optimal tool for analyzing inaccurate and unclear data, as the use of fuzzy methods in analyzing these data can improve accuracy and reliability in estimating relationships between variables. The research estimated a fuzzy linear regression model with a triangular Fuzzy response variable, Crisp explanatory variables, and triangular Fuzzy parameters using the A.R.Arabpour and M.Tata estimation method. The study data was about systolic and diastolic hypertension as a fuzzy response variable and a set of factors affecting it as Crisp, non-fuzzy explanatory variables represented by (age, weight, Glycemia, triglycerides, and cholesterol) for a group of (125) patients obtained from Balad General Hospital in Salah al-Din Governorate, in an attempt to find out the effect of each of these factors under study on systolic and diastolic hypertension, the MATLAB program was used to obtain the results, and the results confirmed the direct relationship between The fuzzy response and the non-fuzzy explanatory variables. The t-test results confirmed the significance of the relationship between them, which explains that systolic and diastolic hypertension may be directly affected by an increase in any of the explanatory variables under study.
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[1] Arabpour, A. R., & Tata, M. (2008). "Estimating the parameters of a fuzzy linear regression model". Iranian Journal of Fuzzy Systems, 5(2), 1-19.
[2] Asai, H. T. S. U. K., Tanaka, S., & Uegima, K. (1982)." Linear regression analysis with fuzzy model". IEEE Trans. Systems Man Cybern, 12, 903-907. DOI: https://doi.org/10.1109/TSMC.1982.4308925
[3] Klir, G. J., & Yuan, B. (1996). "Fuzzy sets and fuzzy logic: theory and applications". Possibility Theory versus Probab. Theory, 32(2), 207-208.
[4] Nowakov´a, J.& Pokorn´y, M. (2013). "Fuzzy Linear Regression Analysis." 12th IFAC Conference on Programmable Devices and Embedded Systems, Czech Republic. DOI: https://doi.org/10.3182/20130925-3-CZ-3023.00079
[5] Pedrycz, W., & Gomide, F. (1998)." An introduction to fuzzy sets: analysis and design". MIT Press. DOI: https://doi.org/10.7551/mitpress/3926.001.0001
[6] 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". DOI: https://doi.org/10.11591/ijai.v12.i2.pp543-551
[7] Ross, T. J. (2010)." Properties of membership functions, fuzzification, and defuzzification". Fuzzy logic with engineering applications, 89-116. DOI: https://doi.org/10.1002/9781119994374.ch4
[8] Shapiro, A. F. (2005)." Fuzzy regression models". Article of Penn State University, 102(2), 373-383.
[9] 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. DOI: https://doi.org/10.1016/S0898-1221(03)90006-X
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