Fuzzy linear regression models can provide an estimated fuzzy number that has a fuzzy membership function. If a point that has the highest membership value from the estimated fuzzy number is not within the support of the observed fuzzy membership function, a decisionmaker can have high risk from the estimate. In this study a new distance, between fuzzy numbers is proposed. On the basis of this ...

We show that analogously to classical probability theory the conditional expectation E ( ? ~ X ) of a fuzzy random variable Y w.r.t. a fuzzy random variable X is w.r.t. a suitable metric the best approximation o f ? by measurable functions ofX. Furthermore, several linear regression functions, i.e. best approximation of ? by linear functions o f z and examples for random LR-fuzzy numbers and Ga...

The parameter h in a fuzzy linear regression model is vital since it influences the degree of the fitting of the estimated fuzzy linear relationship to the given data directly. However, it is usually subjectively preselected by a decision-maker as an input to the model in practice. In Liu and Chen (2013), a new concept of system credibility was introduced by combining the system fuzziness with ...

This paper reports on the development of a learning system for the prediction of dichotomous response variables by combining fuzzy concept with classical regression technique. The algorithm involves linear transformation followed by linear programming. In the algorithm presented it was assumed that the logarithm of the odds (logit) is linearly related to X’s, the independent variables after und...

In fuzzy set theory, it is well known that a triangular fuzzy number can be uniquely determined through its position and entropies. In the present communication, we extend this concept on triangular intuitionistic fuzzy number for its one-to-one correspondence with its position and entropies. Using the concept of fuzzy entropy the estimators of the intuitionistic fuzzy regression coefficients h...

Yu et al. (Fuzzy Sets and Systems 105 (1999) 429) performed general piecewise necessity regression analysis based on linear programming (LP) to obtain the necessity area. Their method is the same as that according to data distribution, even if the data are irregular, practitioners must specify the number and the positions of change-points. However, as the sample size increases, the number of ch...

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and...

Fuzzy sets theory has proven over the years to be a valuable tool for modeling uncertainty in engineering. It is used extensively in control, in expert systems and in rule-based models. However, applications to sensitivity analysis and regression are still few, mainly because there is no appropriate software available. A C++ library of objects has been developed to easily and efficiently introd...

Fuzzy clusterwise regression has been a useful method for investigating cluster-level heterogeneity of observations based on linear regression. This method integrates fuzzy clustering and ordinary least-squares regression, thereby enabling to estimate regression coefficients for each cluster and fuzzy cluster memberships of observations simultaneously. In practice, however, fuzzy clusterwise re...