A novel fuzzy linear regression model based on a non-equality possibility index and optimum uncertainty

Authors:
H. Shakouri G.;R. Nadimi
Affiliations:
Industrial Engineering Department, University of Tehran, Tehran, Iran;Industrial Engineering Department, University of Tehran, Tehran, Iran
Venue:
Applied Soft Computing
Year:
2009

Citing 16
Cited 6

Fuzzy data analysis by possibilistic linear models

Fuzzy Sets and Systems - Fuzzy Numbers
Fuzzy least squares

Information Sciences: an International Journal
Possibilistic linear systems and their application to the linear regression model

Fuzzy Sets and Systems
Evaluation of fuzzy linear regression models

Fuzzy Sets and Systems
Fuzzy linear regression with fuzzy intervals

Fuzzy Sets and Systems
General fuzzy least squares

Fuzzy Sets and Systems
S-curve regression model in fuzzy environment

Fuzzy Sets and Systems
Evaluation of fuzzy linear regression models by comparing membership functions

Fuzzy Sets and Systems
Fuzzy sets as a basis for a theory of possibility

Fuzzy Sets and Systems
A least-squares approach to fuzzy linear regression analysis

Computational Statistics & Data Analysis
Fuzzy Sets and Systems: Theory and Applications

Fuzzy Sets and Systems: Theory and Applications
Fuzzy least-squares linear regression analysis for fuzzy input-output data

Fuzzy Sets and Systems - Information processing
Dependency between degree of fit and input noise in fuzzy linear regression using non-symmetric fuzzy triangular coefficients

Fuzzy Sets and Systems
Monte Carlo methods in fuzzy linear regression II

Soft Computing - A Fusion of Foundations, Methodologies and Applications
A hybrid TSK-FR model to study short-term variations of the electricity demand versus the temperature changes

Expert Systems with Applications: An International Journal
Least squares estimation of a linear regression model with LR fuzzy response

Computational Statistics & Data Analysis

Solving Fuzzy Linear Regression with Hybrid Optimization

ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
A class of fuzzy clusterwise regression models

Information Sciences: an International Journal
An integrated fuzzy regression-data envelopment analysis algorithm for optimum oil consumption estimation with ambiguous data

Applied Soft Computing
A novel nonlinear programming approach for estimating CAPM beta of an asset using fuzzy regression

Expert Systems with Applications: An International Journal
Outlier detection in fuzzy linear regression with crisp input-output by linguistic variable view

Applied Soft Computing
Lattice-based clustering and genetic programming for coordinate transformation in GPS applications

Computers & Geosciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

Various kinds of fuzzy regression models are introduced in the literature and many different methods are proposed to estimate fuzzy parameters of the models. In this study, a new approach is introduced to find the parameters of a linear fuzzy regression, with fuzzy outputs, the input data of which is measured by crisp numbers. Based on a non-equality possibility index, a new objective function is designed and solved, by which a minimum degree of acceptable uncertainty (the h-level or h-cut) is found. Four numerical examples are presented to compare the proposed approach with some other methods. Results show superiority of the new approach based on the criterion used by Kim and Bishu in the cases studied here. A realistic application of the proposed method is also presented, by which the total energy consumption of the Residential-Commercial sector in Iran is modeled using three variables of the GDP, number of the Households and an Energy Price index as inputs (exogenous variables) to the model.