Fuzzy linear regression (FLR) model can be thought of as a fuzzy variation of classical linear regression model. It has been widely studied and applied in diverse fields. When noise exists in data, it is a very meaningful topic to reveal the dependency between the parameter h (i.e. the threshold value used to measure degree of fit) in FLR model and the input noise. In this paper, the FLR model ...

This paper presents a method based on fuzzy regression to analyze fatigue crack growth data, where the variations of the parameters are not only due to measurement errors but also system errors. A membership function is used to describe the system errors. Crack growth data under constant and random amplitude stress are analyzed and the results are compared with conventional least-squares regres...

many ranking methods have been proposed so far. however, there is yet no method that can always give a satisfactory solution to every situation; some are counterintuitive, not discriminating; some use only the local information of fuzzy values; some produce different ranking for the same situation. for overcoming the above problems, we propose a new method for ranking fuzzy quantities based on ...

In this paper, we deal with the ridge-type estimator for fuzzy nonlinear regression models using fuzzy numbers and Gaussian basis functions. Shrinkage regularization methods are used in linear and nonlinear regression models to yield consistent estimators. Here, we propose a weighted ridge penalty on a fuzzy nonlinear regression model, then select the number of basis functions and smoothing par...

The method for obtaining the fuzzy estimates of regression parameters with the help of “Resolution Identity” in fuzzy sets theory is proposed. The -level least-squares estimates can be obtained from the usual linear regression model by using the -level real-valued data of the corresponding fuzzy input and output data. The membership functions of fuzzy estimates of regression parameters will be ...

Coronary heart disease is a major cause of morbidity and mortality in the modern society. Many risk factors for coronary heart disease have been discussed and identified by medical fraternity. However, the magnitudes of risk factors, particularly in predicting the disease are remained unknown and inconclusive. The purpose of this study was to develop fuzzy regression prediction model and to inv...

We evaluated three mathematical procedures to estimate the parameters of the relationship between weight and length for Cichla monoculus: least squares ordinary regression on log-transformed data, non-linear estimation using raw data and a mix of multivariate analysis and fuzzy logic. Our goal was to find an alternative approach that considers the uncertainties inherent to this biological model...