Using Simulation in Fuzzy Regression
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Abstract
The analyst faces some difficulties in analyzing the accuracy of the model, especially when estimating the parameters of the fuzzy regression model. Therefore, variables were created and normally distributed in order to reduce prediction errors by using the most widely used and common method, which is the Box-Muller method, because it relies on the strategy of creating irregularly distributed variables. A standard distribution U (0,1), and then these variables are changed to free random variables that take the standard natural dispersion dimension, which can evaluate the proof parameters and also reduce the prediction error, which is the normal squared error that exists between the expected concentrations and the real concentrations, which in turn explains the accuracy. It shows ambiguity that speaks of weakness in showing expectations. The display performance is superior in terms of accuracy and quality that does not falter as these values decrease.
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References
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