They cover the dynamic fuzzy machine learning model, dynamic fuzzy autonomic learning subspace algorithm, dynamic fuzzy decision tree learning, concept learning based on dynamic fuzzy sets
, semi-supervised multi-task learning based on dynamic fuzzy sets
, dynamic fuzzy hierarchical relationships, and a multi-agent learning model based on dynamic fuzzy logic.
Atanassov & Gargov  introduced the concept of interval-valued intuitionistic fuzzy sets
(IVIFSs) as a further generalization of IFS.
To overcome this problem, the idea of fuzzy sets
was first introduced by Zadeh in 1965.
Many authors have focused on the theory of fuzzy sets
and applications of fuzzy logic in project management.
Using fuzzy logic, intervals or fuzzy sets
can be built for the parameters, which have different degrees of membership.
From the beginning of fuzzy set
theory and its higher order fuzzy sets
, there erupted many opinions about its feasibility and applicability.
For example expert method, brainstorming, simulation, fuzzy sets
They are a generalization of fuzzy sets
, intuitionistic fuzzy sets
[28, 30], interval valued fuzzy set
 and interval-valu e d intuitionistic fuzzy sets
(FSs), proposed by Zadeh , are frameworks to employ when encountering some vagueness.
Consequently Atanassov extended the intuitionistic fuzzy sets
to the interval-valued intuitionistic fuzzy sets
[15, 16], using an interval to express the membership and nonmembership.
were formally introduced fifty years ago by Zadeh (1965) as a tool to provide "a natural way of dealing with the problems in which the source of imprecision is the absence of sharply defined criteria of class membership.
The theory of fuzzy sets
proposed by Zadeh  And Wee  introduced the concept of fuzzy automata following Zadeh [10,11], fuzzy automata and Fuzzy machine theory has been developed by many researchers.