Abstract: This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions.
Abstract: The design of weight is one of the important parts in
fuzzy decision making, as it would have a deep effect on the evaluation
results. Entropy is one of the weight measure based on objective
evaluation. Non--probabilistic-type entropy measures for fuzzy set
and interval type-2 fuzzy sets (IT2FS) have been developed and applied
to weight measure. Since the entropy for (IT2FS) for decision
making yet to be explored, this paper proposes a new objective
weight method by using entropy weight method for multiple attribute
decision making (MADM). This paper utilizes the nature of IT2FS
concept in the evaluation process to assess the attribute weight based
on the credibility of data. An example was presented to demonstrate
the feasibility of the new method in decision making. The entropy
measure of interval type-2 fuzzy sets yield flexible judgment and
could be applied in decision making environment.