Fuzzy Korovkin type Theorems via deferred Cesáro and deferred Euler equi-statistical convergence
June 23, 2023; accepted: October 5, 2023; published online: December 22, 2023.
MSC.
Keywords. Korovkin theorem, fuzzy number,
1 Introduction
The fuzziness is used to deal with imprecise or uncertain information. It measures the imperfection of an example. As a result, the computing by Fuzzy logic approach is based on “degrees of truth” rather than the usual “true or false” (1 or 0) Boolean logic used by the present day computers. Fuzzy logic is used by various types of AI systems and technologies, for instance vehicle intelligence, consumer electronics, medical diagnosis, software, chemicals, aerospace and environment control systems etc. The concept of fuzzy logic was put forward by Zadeh [ 38 ] in 1965, while working on the computer understanding of natural languages. A modified definition of fuzzy numbers was given by Goetschel Jr. and Voxman [ 20 ] . The concept of a sequence of fuzzy numbers was proposed by Matloka [ 25 ] . Nanda [ 27 ] established that the set of convergent sequences of fuzzy numbers is complete. Subrahmanyam [ 34 ] introduced the Cesáro summability of fuzzy numbers. Gal [ 19 ] generalized the classical results of approximation theory to the fuzzy setting. Motivated by this work, Anastassiou [ 6 ] established the fuzzy analogues of many approximation theorems.
Anastassiou
[
8
]
established the basic fuzzy Korovkin type theorem for fuzzy positive linear operators by means of fuzzy Shisha–Mond inequality and also presented the rate of convergence with the aid of fuzzy modulus of continuity. Anastassiou et al.
[
9
]
investigated a Korovkin-type theorem in the fuzzy setting by using a matrix summability method and also examined the rate of convergence with the aid of fuzzy modulus of continuity. Yavuz
[
36
]
presented a fuzzy trigonometric Korovkin type theorem via power series summability method and also established another related approximation theorem with the aid of fuzzy modulus of continuity for functions belonging to
In the past two decades, statistical convergence and its various generalizations have been an active area of research in approximation theory. In the year 1951, Steinhaus
[
33
]
and Fast
[
16
]
independently introduced the notion of statistical convergence to assign a limit to the sequences which are not convergent in the usual sense. Gadjiev and Orhan
[
18
]
established a Korovkin-type approximation theorem for the first time via statistical convergence. Duman and Orhan
[
15
]
derived the Korovkin-type result using the concept of
Nuray and Savaş
[
28
]
proposed the fuzzy analogue of statistical convergence of a fuzzy number valued sequence. Anastassiou and Duman
[
10
]
extended the results obtained in
[
8
]
by using the notion of
The purpose of the present paper is to extend the study carried out in
[
31
]
in the fuzzy environment. We establish the fuzzy Korovkin type approximation theorem for functions in
2 Preliminaries
In our study, we shall need the following definitions.
A fuzzy real number is a function
is normal, i.e., we can find a number such that ; is a fuzzy convex subset, i.e., , and ;for a given
and for any a neighbourhood W of such that , , i.e., is upper semi-continuous on ;the closure of
is compact, where .
Let
for all
A sequence
We denote this convergence by writing
Following
[
1
]
, let
(i)
(ii)
Then the deferred Cesáro mean of
The deferred Euler mean of order
Following
[
31
]
, we call the sequence
has asymptotic density
The sequence
has natural density
Let
where
We denote this convergence by
3 Fuzzy Korovkin Theorem
Let
Let
for all
for
In the following result, we prove the fuzzy Korovkin Theorem via
Let
where
i.e.,
i.e., where,
i.e.,
where
which yields
where
where
For any
and
Then,
Hence using hypothesis (3), the proof is completed.
4 Statistical fuzzy convergence rate
We discuss fuzzy rate of approximation by the operators
Let
where
From [ 8 ] , the fuzzy modulus of continuity is given by:
Let
Let
(i)
(ii)
Then, for all
and hence we obtain
where
where
where
For any
and
Then,
Finally, using (-1) and the assumptions (i) and (ii), we reach the desired assertion.
The authors are extremely grateful to the learned reviewer for a very careful reading of the manuscript and making invaluable suggestions and comments leading to an overall improvement in the presentation of the paper.
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