Priority structure in fuzzy goal programming
Fuzzy Sets and Systems
Fuzzy sets, decision making and expert systems
Fuzzy sets, decision making and expert systems
Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
Information Sciences: an International Journal
On assessing the H value in fuzzy linear regression
Fuzzy Sets and Systems
Fuzzy linear regression with fuzzy intervals
Fuzzy Sets and Systems
A note on a fuzzy goal programming algorithm by Tiwari, Dharmar, and Rao
Fuzzy Sets and Systems
Applying fuzzy linear regression to VDT legibility
Fuzzy Sets and Systems
A generalization of fuzzy goal programming with preemptive structure
Computers and Operations Research
Insight of a fuzzy regression model
Fuzzy Sets and Systems
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The range of a fuzzy regression interval is decided by the collected data and the confidence level h. It is understood that a larger value h in a fuzzy regression equation implies a larger fuzzy regression interval. In order to examine the effect of value h, Moskowitz and Kim developed an analytical method to assess the shape and range of the possibility distribution of a membership function in order to reveal more reliable and realistic results from the fuzzy regression. If the possibility of each datum in the fuzzy regression interval can be easily found, then the regression analysis should be carried out precisely. However, it is complicated to find a proper value h among the types of membership functions of fuzzy parameters and collected fuzzy data as proposed by the analysis process of Moskowitz and Kim. Therefore, in this study, a fuzzy goal programming method is proposed for solving a fuzzy regression equation in which a maximum satisfactory value h in the fuzzy regression interval can be solved. Numerical examples are provided to illustrate our proposed method.