Kim and Bishu (Fuzzy Sets and Systems 100 (1998) 343-352) proposed a modification of fuzzy linear regression analysis. Their modification is based on a criterion of minimizing the difference of the fuzzy membership values between the observed and estimated fuzzy numbers. We show that their method often does not find acceptable fuzzy linear regression coefficients and to overcome this shortcomin...

pedomodels have become a popular topic in soil science and environmentalresearch. they are predictive functions of certain soil properties based on other easily orcheaply measured properties. the common method for fitting pedomodels is to use classicalregression analysis, based on the assumptions of data crispness and deterministic relationsamong variables. in modeling natural systems such as s...

coastal lagoons are complex ecosystems exhibiting a high degree of non-linearity in the distribution and exchange of nutrients dissolved in the water column due to their spatio-temporal characteristics. this factor has a direct influence on the concentrations of chlorophyll-a, an indicator of the primary productivity in the water bodies as lakes and lagoons. moreover the seasonal variability in...

Fuzzy data given by expert knowledge can be regarded as a possibility distribution by which possibilistic linear systems are defined. Recently, it has become important to deal with fuzzy data in connection with expert knowledge. Three formulations of possibilistic linear regression analysis are proposed here to deal with fuzzy data. Since our formulations can be reduced to linear programming pr...

In sensory analysis a panel of assessors gives scores to blocks of sensory attributes for profiling products, thus yielding a three-way table crossing assessors, attributes and products. In this context, it is important to evaluate the panel performance as well as to synthesize the scores into a global assessment to investigate differences between products. Recently, a combined approach of fuzz...

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...

In this paper, we have presented a new method to evaluate fuzzy linear regression models based on Tanaka’s approach, where both input data and output data are fuzzy numbers, using Tw-based fuzzy arithmetic operations. This method simpli3es the computation of fuzzy arithmetic operations. General linear program is applied to derive the solutions. We also prove scale-independent property of our mo...

Previous studies on fuzzy linear regression analysis have a common characteristic of increasing spreads for the estimated fuzzy responses as the independent variable increases its magnitude, which is not suitable for general cases. This paper proposes a two-stage approach to construct the fuzzy linear regression model. In the 2rst stage, the fuzzy observations are defuzzi2ed so that the traditi...

In this paper, we aim to extended the constraints of Tanaka’s model. Applied coefficients of the fuzzy regression by them is the symmetric triangular fuzzy numbers, while we try to replace it by more general asymmetric trapezoidal one. Possibility of two asymmetric trapezoidal fuzzy numbers is explained by possibility distribution. Two different models is presented and a numerical example is gi...