Abstract
In this article, we apply a new class of fuzzy control functions to approximate a Cauchy additive mapping in fuzzy Banach space (FBS). Further, considering the unital FBS (UFBS), we will investigate the isomorphisms defined in this space. By introducing several specific functions and choosing the optimal control function from among these functions, we evaluate the Cauchy–Optimal stability (C–O-stability) for all defined mappings.
Keywords:
Cauchy additive mapping; Jensen additive mapping; stability; isomorphism; fuzzy Banach space; unital space MSC:
46L05; 47B47; 47H10; 46L57; 39B62
1. Introduction
The topic of stability first started with Ulam’s famous question about additive mappings in 1940. After that, Hyers and Rassias, by expanding this topic, presented new definitions of stability for additive mappings and continuous maps, which were known as Hyers–Ulam and Hyers–Ulam–Rasias stability, respectively [1,2,3]. Since then, many researchers have conducted extensive research on the issue of stability for functional equations in different spaces. For example, in 1980, the stability of homomorphism equations was studied.
Researchers in [4] considered the function , which is defined as follows
and in [5], considered the function , which is defined as follows
on the abelian group and the Banach space . They investigated the stability of two types of functions. In such a way that if for the function , we have , then there is a unique additive mapping such as such that , and if for the function , we have , then there is a unique additive mapping such as such that . Additionally, in [6,7,8,9,10,11,12], authors have investigated different equations using fixed point theory in classical and fuzzy spaces. In 1965, the theory of fuzzy sets was introduced in [13]. After that, this new theory was applied to classical concepts. The concept of the fuzzy norm was introduced by Katsaras in 1984 [14]. Moreover, Kramosil and Michalek (1975) introduced the new concept of fuzzy metric space and provided many results. In 1994, George and Veeramani introduced a stronger form of fuzzy metric space [15,16]. Afterward, many mathematicians studied fixed-point theorems in related spaces. In [10], the authors considered a nonlinear single fractional differential equation and applied an alternative fixed-point theorem to prove the existence of a unique solution and the multiple stability for the NS–ABC–FDE in the symmetric matrix-valued FBS in [17], considering fuzzy measure theory and matrix-valued fuzzy norm spaces, they study a differential system of non-autonomous cellular neural networks with mixed delays. Specific functions are among the most widely used functions in mathematics and other sciences, which have attracted the attention of many researchers today. These functions are used in various fields, such as physical sciences, engineering, probability theory, decision theory, artificial intelligence, pattern recognition, image processing, etc. The Mittag–Leffler function, Gauss hypergeometric function, Wright function, H-Fox function, and aggregation functions are the most important types of these functions. Since our effort in the stability issue is to achieve the best approximation, we achieve this important goal by selecting the most optimal function among these specific functions and using it as the control function [8,9,10]. In the following, we will explain the different parts of this article:
In the first section, we state all the basic concepts, including definitions, lemmas, and basic theorems needed for the main steps. In the second section, we do the main proofs. In this way, considering the vector spaces and defining the function , we consider the following equations
for all and show that the function is an additive function. In the following, we prove the stability of functional Equations (1)–(3) by considering FBS. Additionally, all the proofs are done considering the UFBS for the isomorphisms defined in these spaces. In the third section, by choosing the aggregation function as the optimal control function, we investigate the O-stability of the functional equations. We end this article in the last section entitled Conclusion.
2. Basic Concepts
We first introduce the required spaces. These spaces are used in all parts of the article [6,8,10]. From here on, we consider , , and .
Definition 1.
On the interval , we define as follows
such that for any , we have , , and . Further, means that for every .
Definition 2.
We consider the mapping ⊛ from to . If for each we have , , , , we say that ⊛ is a generalized t-norm or briefly GTN. Additionally, we consider sequences and that converge to and . If we have , then ⊛ is a CGTN.
There are different types of CGTN—minimum CGTN, product CGTN, and Lukasiewicz CGTN can be mentioned among the most important of them. In this work, we choose the minimum CGTN , which is defined as follows:
We also provide the definition of product CGTN and Lukasiewicz CGTN as follows:
In the following, we provide examples of these CGTNs.
Example 1.
()
- ()
- ()
In the following, we will define the features of the matrix-type fuzzy functions and the matrix-type fuzzy norms spaces.
Definition 3.
The MVFF is increasing and continuous, for every and , if and only if for all and where is the MVFF and ⪯ is the relation defined for this type of function.
Definition 4.
Consider the linear space , CGTN ⊛ and the MVFF , we define , which is called an MVFN-S and has the following properties,
- if and only if for ;
- for all and ;
- for all and any ;
- for any .
When an MVFN-S is complete, we denote it by MVFB-S.
In the following, we investigate optimal stability by introducing a new optimal control function. For this purpose, we go to the definition of the aggregation function. Next, we provide a brief introduction of the special functions used in the optimal control function [6].
Definition 5.
If for any and and an idempotent function , we have , then the -ary is a generalized aggregation function where . For and each , we have and for the convenience of writing we can remove ( indicates the number of function variables).
The famous functions, i.e., arithmetic mean function, projection function, order statistic function, median function, and minimum and maximum functions, are among the important functions of aggregation type. In [6], the authors showed a control function made by minimum aggregation function is the optimal controller. The minimum (MIN) is the smallest generalized aggregation function, and it is defined as follows
Therefore, by studying [6,10], we consider the following function as the optimal controller:
where
In the proofs, we use the symbol instead of the control function . Additionally, all the variables in the above function are considered as . In the following, we introduce special functions used in function [6,10].
Definition 6.
The real exponential function is defined by the following power series
since the radius of convergence of this power series is infinite, this definition is, in fact, applicable to all complex numbers .
Definition 7.
For , , the Mittag-Leffler functions are defined as follows
where is the famous gamma function and , are the one- and two-parameter Mittag-Leffler functions, respectively.
Definition 8.
For , the Wright function is defined as follows
such that it is of the order.
Definition 9.
H-Fox function for , , and is defined as follows
where is a path that is deleted and , , , and . For these functions, there is a condition that if and only if , if and only if and if and only if . Further, .
Definition 10.
Considering , the Gauss hypergeometric function is defined as follows
These functions are used in all the theorems presented in Section 4.
Considering the two vector spaces and along with FNS, for all , we define the following equations by considering the mapping :
For the field , we consider two UFBSs, and along with the unique terms , and FNSs. For each and all and for the mapping , we consider the following equation
We assume that is a function that for all , we define in the following 4 cases:
and we consider these functions to prove our results.
3. C–O-Stability for CJAM and Isomorphisms in FBS and UFBS
Theorem 1
Proof.
Proposition 1
([7]). If are the functions used in theorem 1, then these functions are of Cauchy–Jensen additive mapping (CJAM) type. If we write and in (3), then we have the following Cauchy–Jensen additive mapping (CJAM), respectively
Theorem 2.
Considering the mapping and function (13), for each , if we have
then, there exists a unique AM such that for all
Proof.
In the assumption of (20), we put . Therefore, for all , we have
Then, for each
therefore, for all and for each and with , we have
Therefore, according to (13) and (23), for all , the sequence is a Cauchy sequence, and since is complete, the convergence of this sequence is the result. Therefore, for each , we define the mapping as . Due to equations (13) and (20), for each , we get
and then, . Now, using Theorem 1, we conclude that is a CAM. If we consider (23) and assume that and we get limit when , we reach (21). Next, we prove the uniqueness of . For this purpose, we consider another CJAM and assume that it applies to (21). Therefore, we have the following inequality for every , which tends to zero when ,
and this means . □
Theorem 3.
Proof.
Therefore, according to (14) and (28), for all , the sequence is a Cauchy sequence, and since is complete, the convergence of this sequence is the result. Therefore, for each , we define the mapping as . Due to the (14) and (20), for each , we get
and then, . Now, using Theorem 1, we conclude that is a CAM. If we consider (28) and assume that and we get limit when , we reach (21). To prove the uniqueness, we repeat all the steps taken in Theorem 2, and the proof is finished. □
Theorem 4.
Proof.
In the assumption of (20), we put . Therefore, for all , we have
Then, for each
and therefore, for all and for each and with , we have
Theorem 5.
Proof.
Theorem 6.
Proof.
In the (33), we put . Therefore, for all , we have
To continue the proof, all the steps we have to go through are similar to the steps of Theorem 2. □
Theorem 7.
Proof.
Using (34) and for each , we have
the continuation of the proof process is similar to the proof process of Theorems 2 and 3. □
Theorem 8.
Considering the mapping and function (14), for each , if for all , we have
then, there exists a unique AM such that for all
Proof.
In the (35), we put . Therefore, for all , we have
The continuation of the proof process is similar to the proof process of Theorem 2. □
Theorem 9.
Proof.
Using (37) and for each , we have
the continuation of the proof process is similar to the proof process of Theorems 2 and 3. □
Theorem 10.
Proof.
All the steps we have to go through to prove this theorem are similar to the steps of Theorems 2 and 4. □
Theorem 11.
Proof.
All the steps we have to go through to prove this theorem are similar to the steps of Theorems 2, 4, and 5. □
Theorem 12.
Considering the bijective multiplicative mapping and the function which satisfies (13), if for every and for all , we have
and
then, the BMM Ψ is an isomorphism.
Proof.
To start the proof, we consider hypothesis (38) with . According to Theorem 2, for every , there exists a unique AM with the mapping
such that it holds in (21). For all and each , using (38) and (39), we get
Therefore, and for all and all , . Considering that is additive, for every and all , we have and this means that is an -linear mapping. Now we use the multiplicative property of . As a result, for each , we have
4. C–O–M-Stability for CJAM and Isomorphisms in FBS and UFBS
In this section, in Theorems 13–22, the function from to , and also .
Theorem 13.
If we have the following condition for every and
then, we can say that there is a unique AM , such that for every
Proof.
To prove it, it is enough to define the function as and put it in Theorem 2. □
Theorem 14.
If we have the following condition for every and
then, we can say that there is a unique AM , such that for every
Proof.
To prove it, it is enough to define the function as and put it in Theorem 2. □
Theorem 15.
If we have the following condition for every and
then, we can say that there is a unique AM , such that for every
Proof.
To prove it, it is enough to define the function as and put it in Theorem 3. □
Theorem 16.
If we have the following condition for every and
then, we can say that there is a unique AM , such that for every
Proof.
To prove it, it is enough to define the function as and put it in Theorem 3. □
Theorem 17.
If we have the following condition for every and
then, we can say that there is a unique AM , such that for every
Proof.
To prove it, it is enough to define the function as and put it in Theorem 4. □
Theorem 18.
If we have the following condition for every and
then, we can say that there is a unique AM , such that for every
Proof.
To prove it, it is enough to define the function as and put it in Theorem 5. □
Theorem 19.
If we have the following condition for every and
then, we can say that there is a unique AM , such that for every
Proof.
To prove it, it is enough to define the function as and put it in Theorem 8. □
Theorem 20.
If we have the following condition for every and
then, we can say that there is a unique AM , such that for every
Proof.
To prove it, it is enough to define the function as and put it in Theorem 8. □
Theorem 21.
If we have the following condition for every and
then, we can say that there is a unique AM , such that for every
Proof.
To prove it, it is enough to define the function as and put it in Theorem 9. □
Theorem 22.
If we have the following condition for every and
then, we can say that there is a unique AM , such that for every
Proof.
To prove it, it is enough to define the function as and put it in Theorem 9. □
Theorem 23.
We consider the BMM function such that for every and all , it applies to the following inequalities
where and . Then the BMM Ψ is an isomorphism.
Proof.
To prove it, it is enough to define the function as and put it in Theorem 12. □
Theorem 24.
We consider the BMM function such that for every and all , it applies to the following inequalities
where and . Then the BMM Ψ is an isomorphism.
Proof.
To prove it, it is enough to define the function as and put it in Theorem 12. □
5. Conclusions
The issue of the stability of equations has attracted the attention of many authors in the last few decades. In this article, we have tried to present a generalization of previous works in this field. For this purpose, we have first defined a new space called the matrix value fuzzy Banach spaces. In the stability of equations, the goal is to obtain the best approximation. Since the control function plays an important role in this field, we have selected the best and most optimal controller from among the specific functions. Choosing the aggregation control function brings us closer to the appropriate approximation. We have done all these proofs for functional Equations (1)–(3) and isomorphisms in FBS and UFBS.
Author Contributions
All authors contributed to the study’s design and coordination, drafted the manuscript, and read and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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