Main Article Content

Fatima Othman Eatiah Al-Abadi
Prof. Dr. Sahera Hussein Zain Al-Thalabi


The researcher faces a lot of problems when testing the accuracy of the model to estimate the parameters of the fuzzy regression model, and to remedy this problem, the prediction error was reduced by generating variables that follow a normal distribution using the most famous and common method, which is the (Box-Muller) method, which depends on the method of generating random variables that follow the standard uniform distribution U(0,1), and then these variables are converted into independent random variables that follow the standard normal distribution to estimate the parameters of the model and with the aim of reducing the prediction error between the expected and actual concentrations This indicates model accuracy and model blur that represents uncertainty in model predictions. The lower these values, the better the model performs in terms of accuracy and reliability.

Article Details

How to Cite
Fatima Othman Eatiah Al-Abadi, & Prof. Dr. Sahera Hussein Zain Al-Thalabi. (2024). USING SIMULATION TO ESTIMATE A FUZZY REGRESSION MODEL. Galaxy International Interdisciplinary Research Journal, 12(1), 177–182. Retrieved from


Abbas, M. S. & Al-Metwally, A. S., (2021) "Using the Fuzzy Linear Regression Model in Estimating the Impact of the Dollar Exchange Rate on the Gross Domestic Product in Iraq", Journal of Administration and Economics - Al-Mustansiriya University, Volume and Issue 129.

Bajaj, R. K., Garg, G., Hooda, D.S., 2009, " Estimating Regression Coefficients in a Restricted Fuzzy Linear Regression Model",, 27/10/2023, 7:27P.M.

Bisserier. A,& Galichet.S,Boukezzoula. R, 2008, "Fuzzy Piecewise Linear Regression" , HongKong, China. pp. 2089-2094.

Charfeddine, S. , Mora-Camino, F. & Coligny , M. ( 2014) ," Fuzzy linear regression : application to the estimation ofair transport demand" , ,Russia. pp 350-359. hal-01022443.

Dahmani,s., Maamoun, M., Zerar,G., Chabini, N.& Beguenane,R., 2022, " An Efficient FPGA-Based Gaussian Random Number Generator Using an Accurate Segmented Box-Muller Method ", IEEE Access, Citation information: DOI 10.1109/ACCESS.2023.3289432

Donoso. S, 2006, "Quadratic Programming Models for Fuzzy Regression", Nicol´asMar´ın and M. Amparo Vila Department of Computer Science and A. I. University of Granada - 18071 - Granada – Spain

Elias, H. M. & Sabbagh, Heba Ali Taha (2006), "Fuzzy Regression Analysis", Iraqi Journal of Statistical Sciences, No. 10, pp. (61-84 .(

Karakasidis.T,Georgiou.D, Nieto.J, 2012," Fuzzy regression analysis: An application on tensile strength of materials and hardness scales", Journal of Intelligent & Fuzzy Systems 23 ,177–186

Mohammed,J.M., Abbas. M.S,2018, "Estimation Nonparametric Fuzzy Regression model using simulation", Economics & Administration of Journal The ,41,Volume115.

Mosleh. M, Otadi. M, Abbasbandy. S, 2011,". Fuzzy polynomial regression with fuzzy neural networks", Applied Mathematical Modelling, Volume 35, Issue 11, Pages 5400-5412

Nasrabadi, M.M, Nasrabadi, E.& Nasrabady, A.R., (2005) " Fuzzy linear regression analysis: a multi-objective programming approach", Applied Mathematics and Computation 163, P: 245–251.