IEEE Systems, Man and Cybernetics Magazine - July 2022 - 52

model are simplified by reducing the 3D T2F set into 2D
IT2 fuzzy models.
The current article presents a new regression model of
T2F by considering the more general form of T2F membership
functions and, thus, avoids high complexity. The performance
of the developed model is evaluated using the
Taiwan Stock Exchange Capitalization Weighted Stock
Index (TAIEX) and COVID-19 forecasting
data sets. Our developed
model reached the highest performance
when compared to the
other state-of-the-art techniques.
Our developed method is ready to
be tested with more uncertain data
and has the potential to predict the
weather and stock values.
Fuzzy Sets
Real-life information usually has
vagueness; consequently, Zadeh [1]
introduced the type-1 fuzzy set
(T1FS) to model this vagueness.
T2F systems (T2FSs) were introduced
since T1FS cannot model
that information with high-level uncertainty [2]. Despite
the ability of the T2F system in modeling natural phenomena,
these models were not considered in the beginning
due to their high computational complexity. IT2 fuzzy
sets (IT2FSs) were employed more than other T2F models
because of their abilities in modeling phenomena and
also their simplicity of calculations. Nevertheless, it is
not easy to explain the application of any T2FS. However,
T2FS has various applications in electrical energy, business
and finance, health care, automatic control, and
medical applications [3]-[9].
Finding a relation between two or more variables is
simpler, more comprehensible, and practical models of
type-2 models.
A few researchers have applied T2FLR to model the
T2FS has various
applications in
electrical energy,
business and
finance, health
care, automatic
control, and medical
applications.
issues [14], [15], [22]-[26]. A T2F qualitative regression
technique was introduced by Wei and Watada [69]. They
employed a general T2F number, whereas they employed
IT2. Poleshchuk et al. [25] posited a model for IT2FS
according to the least-squares
approximation. Hosseinzadeh et al.
[27] proposed a model with certain
input and output in accordance
with weighted goal programming
(WGP). However, in this model,
only a few points of fuzzy functions
converge.
Few studies have focused on
the T2F c-regression (T2FCR)
models, wh ich are di fferent
from T2FR [28],
[29]. None of
the previous models are able to
provide a good model of T2FR,
and all of them have reduced
the T2FR model to several points
of the membership funct ion
closer together. An IT2FR model was suggested by
Shafaei et al. [30], based on
a - cuts concepts, reaching
good results [31].
In all previous T2FR models, the secondary membernecessary
for many applications. Regression analysis
is a statistical gadget for discovering these relations.
Since people use linguistic terms to judge and evaluate
things in their lives, FR models are suitable for
these applications.
There are two types of FR models. The first type is the
fuzzy linear regression (FLR) model introduced by Tanaka
[7]-[9]. Instead of finding fuzzy coefficients in regression
models, this model solves a linear programming
problem. The second type is based on the least square
error method [10].
IEEE Systems, Man and Cybernetics Magazine - July 2022

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