Abstract
A possibilistic regression model is an interval-type model. An intervaltype
model intuitively helps us to understand the possibilities of the target system.
The data distribution defines the possibility interval of the system, which may hinder
our understanding of the analysis results. Improved models have reported using outlier
problem approaches. We propose models to deal with the vagueness included in a
possibility grade derived from a possibilistic regression model and samples. Unfortunately,
the results obtained by the proposed models were not as expected. Then, the
improved model was proposed to handle the vagueness included in possibility grades.
The numerical example confirmed that the proposed model could eliminate the influence
of unusual samples and describe the possibilities of a focal system. The paper
reports the improved model and the results by using a numerical example.
