1. Introduction
Fuzzy sets theory epic started in 1965 when Lotfi A. Zadeh published the paper “Fuzzy Sets” [
1], received with distrust at first but currently cited by over 95,000 papers. Mathematicians have been constantly concerned with adapting fuzzy sets theory to different branches of mathematics, and many such connections have been made. The beautiful review paper published in 2017 [
2] is a tribute to Lotfi A. Zadeh’s contribution to the scientific world and shows the evolution of the notion of fuzzy set in time and its numerous connections with different topics of mathematics, science, and technique. Another great review article published as part of this Special Issue dedicated to the Centenary of the Birth of Lotfi A. Zadeh [
3] gives further details on the development of fuzzy sets theory and highlights the contributions of Professor I. Dzitac who has had Lotfi A. Zadeh as mentor. In 2008, he edited a volume [
4], tying his name to that of Lotfi A. Zadeh for posterity.
The first applications of fuzzy sets theory in the part of complex analysis studying analytic functions of one complex variable were marked by the introduction of the concept of fuzzy subordination in 2011 [
5]. The study was continued, and the notion of fuzzy differential subordination was introduced in 2012 [
6]. All the aspects of the classical theory of differential subordination which are synthesized in the monograph published in 2000 [
7] by the same authors who have introduced the notion in 1978 [
8] and 1981 [
9] were then adapted in light of the connection to fuzzy sets theory. At some point, fuzzy differential subordinations began to be studied in connection with different operators with many applications in geometric function theory as it can be seen in the first papers published starting with 2013 [
10,
11,
12]. The topic is of obvious interest at this time, a fact proved by the numerous papers published in the last 2 years, of which we mention only a few [
13,
14,
15,
16].
In this paper, fuzzy differential subordinations will be obtained using the differential operator defined and studied in several aspects in [
17,
18].
The basic notions used for conducting the study are denoted as previously established in literature.
Let and denote by the class of holomorphic functions in the unit disc U. Let be the subclass of normalized holomorphic functions writing as . When and , denote by writing . The class of convex functions is obtained for when the class denoted by contains convex functions of order .
The definitions necessary for using the concept of fuzzy differential subordinations introduced in previously published cited papers are next reminded.
Definition 1 ([
19])
. A pair , where and is called fuzzy subset of X. The set A is called the support of the fuzzy set and is called the membership function of the fuzzy set . One can also denote . Remark 1. If , then
For a fuzzy subset, the real number 0 represents the smallest membership degree of a certain to A and the real number 1 represents the biggest membership degree of a certain to A.
The empty set is characterized by , , and the total set X is characterized by , .
Definition 2 ([
5])
. Let , be a fixed point and let the functions . The function f is said to be fuzzy subordinate to g and write or , if the conditions are satisfied:(1)
(2) ,
Definition 3 ([
6] (Definition 2.2))
. Let and h univalent in U, with . If p is analytic in U, with and satisfies the (second-order) fuzzy differential subordinationthen p is called a fuzzy solution of the fuzzy differential subordination. The univalent function q is called a fuzzy dominant of the fuzzy solutions of the fuzzy differential subordination, or more simple a fuzzy dominant, if , , for all p satisfying (1). A fuzzy dominant that satisfies , , for all fuzzy dominants q of (1) is said to be the fuzzy best dominant of (1). Lemma 1 ([
7] (Corollary 2.6g.2, p. 66))
. Let andIfthen Lemma 2 ([
20])
. Let h be a convex function with , and let be a complex number with . If with , , an analytic function in U andthenwhere The function q is convex and is the fuzzy best dominant. Lemma 3 ([
20])
. Let g be a convex function in U and let where and n is a positive integer.If is holomorphic in U andthenand this result is sharp. Sălăgean and Ruscheweyh differential operators are well known in geometric function theory for the nice results obtained by implementing them in the studies. Their definitions and basic properties are given in the next two definitions and remarks.
Definition 4 (Sălăgean [
21])
. For , , the operator is defined by , Remark 2. If , , then , .
Definition 5 (Ruscheweyh [
22])
. For , , the operator is defined by , Remark 3. If , , then , .
The next definition shows the operator used for obtaining the original results of this paper, defined in a previously published paper. Two remarks regarding it are also listed.
Definition 6 ([
17])
. Let , . Denote by the operator given by Remark 4. is a linear operator and if , , then
Remark 5. For , , and for , ,
For , , and for ,
Definition 7 ([
11])
. Let where is the membership function of the fuzzy set associated to the function f.The membership function of the fuzzy set associated to the function coincides with the membership function of the fuzzy set associated to the function f, i.e., , .
The membership function of the fuzzy set associated to the function coincide with the half of the sum of the membership functions of the fuzzy sets , respectively , associated to the function f, respectively g, i.e., , .
Remark 6. As and , it is evident that
,
2. Main Results
First, a new fuzzy class of analytic functions is defined using the operator given by Definition 6.
Definition 8. The fuzzy class denoted contains all functions which satisfy the fuzzy inequalitywhen , and . The first result for this class is related to its convexity.
Theorem 1. The set is convex.
Proof. Consider the functions
For obtaining the required conclusion, the function
must be part of the class
, with
and
non-negative such that
We next show that ,
, , and
.
From Definition 7 we obtain that
As we have and , . Therefore, and we obtain that , which means that and is convex. □
A fuzzy subordination result is given in the next theorem and a related example follows.
Theorem 2. Considering the convex function in U denoted by g and defining with , , if and , then the fuzzy differential subordinationimpliesand this result is sharp. Proof. Using the definition of function
, we obtain
Differentiating (
5) with respect to
z, we have
and
Differentiating (
6) we have
Using (
7), the fuzzy differential subordination (
4) becomes
If we denote
then
Replacing (
9) in (
8) we obtain
Using Lemma 3, we have
and
g is the best dominant. We have obtained
□
Example 1. If , then implies where .
Several fuzzy subordination results are contained in the next theorems and corollaries. Some are followed by examples.
Theorem 3. Let and . If , and , thenwhere Proof. As function
h given in the theorem is convex, we can use the same arguments as in the proof of Theorem 2. Interpreting the hypothesis of Theorem 3, we read that
where
is given by (
9).
By applying Lemma 2, the following fuzzy inequality is obtained:
where
Using the hypothesis of convexity for function
g, it is known that
is symmetric with respect to the real axis and we can write
and
From (
11) we deduce inclusion (
10). □
Theorem 4. Let g be a convex function with and define the function
If a function satisfiesfor and , then we obtain the fuzzy differential subordinationand this result is sharp. Proof. Using Remark 4 concerning the operator
, we can write
Consider
We deduce that .
Let for Differentiating the expression we obtain
Using this result in (12), we can write
We can now apply Lemma 3 and obtain
Therefore,
and this result is sharp. □
Theorem 5. Let h be a convex function of order with If a function satisfiesfor and , thenwhere is convex and is the fuzzy best dominant. Proof. Let
As
from Lemma 1, we obtain that
is a convex function and verifies the differential equation associated to the fuzzy differential subordination (
13)
, therefore it is the fuzzy best dominant.
Differentiating, we obtain
and (
13) becomes
Corollary 1. Let a convex function in U, . If , and verifies the fuzzy differential subordinationthenwhere q is given by The function q is convex and it is the fuzzy best dominant. Proof. We have with and , therefore
Following the same steps as in the proof of Theorem 5 and considering
, the fuzzy differential subordination (
14) becomes
By using Lemma 2 for
, we have
, i.e.,
and
□
Example 2. Let with and .
As , the function h is convex in U.
Let , . For , , , we obtain Then, and
We have
Using Theorem 5 we obtaininduces Theorem 6. Define the function , , using g a convex function in U with . If a function satisfiesfor and then we obtain the sharp fuzzy differential subordination Proof. For , we have
We have and we obtain .
By using Lemma 3, we have
We obtain
□
Theorem 7. Given a convex function g with , define function
If we take and and a function satisfyingthen the sharp fuzzy differential subordination results Proof. Using the definition of operator
, we get
Using this result in (
16), we obtain
which can be easily transformed into
Let
We deduce that
.
Using the notation in (
18), the fuzzy differential subordination becomes
By using Lemma 3, we have
and this result is sharp. □
Theorem 8. Let h be a convex function of order which satisfies If a function satisfiesfor and , then the fuzzy differential subordination can be written aswith being convex and the best fuzzy dominant. Proof. As
h is a convex function of order
, Lemma 1 can be applied and we have that
is a convex function and verifies the differential equation associated to the fuzzy differential subordination (
19)
, therefore it is the fuzzy best dominant.
Using the properties of operator
and considering
, we obtain
As
, using Lemma 3, we deduce
Corollary 2. Consider the special case when using the convex function , where .
If , and satisfies the differential subordinationthenwhere q is given by for The function q is convex and it is the fuzzy best dominant. Proof. Following the same argumentation as for the proof of Theorem 7 and taking
, the fuzzy differential subordination (
20) becomes
By using Lemma 2 for
, we have
, i.e.,
and
□
Example 3. Let a convex function in U with and (see Example 2).
Let , . For , , and , we obtain and We also obtain
We have
Using Theorem 8 we obtaininduce