1. Introduction
We all know that the research about set-valued theory has been a hot topic in recent years. In the real world, sometimes we cannot get accurate single-valued data. For example, if we describe the price of the stock on one day, the single-point value is limited to characterizing the changes of the stock’s price in a day. So it is more appropriate to use set value to describe the price of stock. Many scholars have done a lot of beautiful work on set-valued theory. Arrow and Debreu [
1] in 1954 introduced the concept of set-valued random variables and Aumann [
2] in 1965 introduced the integral. Hiai and Umegaki [
3] gave the definition of conditional expectation of set-valued random variables in 1977. Beer [
4] discussed the topologies of closed and closed convex sets in 1993.
It is well known that limit theorems are important in probability and statistics. Since the 1970s, many scholars have studied the strong law of large numbers (SLLN, for short) for set-valued random variables. Artstein and Vitale [
5] demonstrated an SLLN for compact set-valued random variables in
. Puri and Ralescu [
6] obtained the SLLNs for independent and identically distributed compact convex set-valued random variables in Banach spaces. Taylor and Inoue discussed the convergence theorems for independent and weighted sums of set-valued random variables, respectively, in [
7,
8]. Fu et al. [
9], Casting et al. [
10] and Li and Guan [
11,
12] also studied the limit theorems for set-valued random variables in different kinds of conditions. All the above studies were discussed in the sense of the clear distance between sets. However, in real life, the distance between two objects sometimes is uncertainty, and it may not be easy to describe with explicit distance. Only words with fuzzy language such as “very close” and “very far” can be used to describe them. So it is necessary to study the fuzzy metric.
George and Veeramani firstly gave the definition of fuzzy metric in [
13] and discussed the conditions of completeness and separability in fuzzy metric space in [
14]. Later, Gregori et al. did a lot of research work on fuzzy metric space in [
15,
16,
17,
18]. Minana et al. [
19] and Wu et al. [
20] discussed the properties of fuzzy metric space. Morillas et al. [
21,
22] discussed the application of fuzzy metric in image filter and other practical fields. In addition, Saadati and Vaezpour [
23] defined fuzzy normed space, studied its properties and discussed the relationship between fuzzy norm and fuzzy metric, thus defining fuzzy Banach space. There are also some scholars who elaborated the fuzzy metric in different ways [
24,
25]. However, the elements in the above papers are still single-point values. In [
26], Ghasemi et al. extended the fuzzy metric space to the case of set-valued and fuzzy set-valued random variables and discussed the laws of large numbers for fuzzy set-valued random variables, but the authors did not give the complete statement and definition of fuzzy metric and fuzzy norm for sets. In this paper we consider the definition of fuzzy metric for sets, discuss its properties and study the SLLNs for set-valued random variables in fuzzy metric space.
This article was organized as follows. In
Section 2, we mainly introduce the concepts and notations on set-valued random variables. In
Section 3, we shall introduce the concepts of fuzzy metric and fuzzy norm on
and discuss the properties. In
Section 4, we prove the SLLN for independent and identical distributed compact set-valued random variable and SLLN for independent, tight set valued random variables. The convergence is about fuzzy metric
induced by
.
2. Preliminaries on Set-Valued Random Variables
Throughout this paper, we assume that
is a complete probability space (i.e., every
-null set belongs to
-field
; for the detail about complete probability space, readers can refer to [
27] (Page 55, Theorem B).
is a Banach space in
,
(
,
) is the family of all nonempty closed (compact and convex, respectively) subsets of
. For a set
,
denotes the convex hull of
A.
Let
A,
B and
. Define the Minkowski addition and scalar multiplication as
The Hausdorff metric on
is defined by
for
. For an
A in
, let
.
The metric space
is complete and separable, and
is a closed subset of
(cf. [
28], Theorems 1.1.2 and 1.1.3).
For each
, the support function is defined by
where
is the dual space of
.
Now we recall the definition of total gHukuhara difference in [
29], define
We say that is minimal with respect to set magnitude (norm-minimal for short) if no exists with . The set of all elements of with the norm-minimality property will be denoted by .
Let
be given. The following convex set always exists and is unique
where
means closure convex hull of
A;
is called the total gHukuhara difference of
A and
B.
The mapping is called a set-valued random variable if, for each open subset O of , .
For each set-valued random variable
F, the expectation of
F is defined by
where
is the usual Bochner integral in
(the family of integrable
-valued random variables), and
.
Let () denote the space of all integrably bounded compact (compact and convex) random variables. We denote it as (, respectively) for simplicity.
For any , if and only if .
Let
be Borel field of
. Define the sub-
-field
by
, where
, then
is a sub-
-field of
. Set-valued random variables
are said to be independent if
are independent. For more concepts and results of set-valued random variables, readers may refer to the books [
28,
29,
30,
31].
3. Fuzzy Metric Space
In this section, we shall introduce the definition of fuzzy metric and fuzzy norm on , and discuss their properties.
Definition 1. (cf. [32]) A t-norm is a binary operator , such that ; the following conditions are satisfied: - (1)
;
- (2)
;
- (3)
, whenever and ;
- (4)
.
When ∗ is a continuous function on , it is said to be continuous.
Definition 2. Let be an arbitrary non-empty set, ∗ is a continuous t-norm. The 3-tuple is said to be a fuzzy metric space for sets if M is a fuzzy set on , satisfying the following conditions for and :
- (1)
, ;
- (2)
, ;
- (3)
;
- (4)
;
- (5)
is continuous.
M is called fuzzy metric on .
Definition 3. Let be a vector space and ∗ a continuous t-norm. The 3-tuple is said to be a fuzzy normed space for sets if N is a fuzzy set on , satisfying the following conditions for and :
- (1)
, ;
- (2)
, ;
- (3)
;
- (4)
;
- (5)
is continuous;
- (6)
.
N was called fuzzy norm for sets on .
Remark 1. - (i)
can be thought of as the degree of nearness between A and B with respect to t. We identify with for . If the distance between A and B is ∞, then we denote for . If are single point values, then M degenerates to the fuzzy metric of single-valued elements.
- (ii)
It is obvious that Definition 3 (3) means .
- (iii)
If , , then for .
Indeed, if
, there exists
, such that
; thus
. Then we can have
For example, let
,
; define
In this case, it is easy to show that is a fuzzy metric space. is a fuzzy normed space. M is called fuzzy metric induced by . N is called the fuzzy norm induced by . There are also some other kinds of fuzzy metrics and fuzzy norm induced by , we denote the fuzzy metrics that were induced by as and fuzzy norm induced by as .
From Definition 2, we can easily get the following property.
Theorem 1. Let M be a fuzzy metric for sets on , then is nondecreasing with respect to t.
Proof. Let
,
. According to Definitions 1 and 2 we can have
So it is not decreasing with respect to t. ☐
Next is the definition of convergence for sets in fuzzy metric space.
Definition 4. Let be sets in . M is a fuzzy metric on . If , as , then is said to be convergent to A in fuzzy metric M.
Theorem 2. Let be a monotone decreasing (increasing) sequence, and , then for , we have Furthermore if M is induced by N as , we can also have Proof. Assume
is a monotone decreasing sequence; then there exists a constant
such that when
,
monotone increases (or decreases) to
; then by remark I (3), there exists
such that
, and
monotone is non-increasing (or non-decreasing) when
; then
exists. Take
for
, where
monotone increases (or decreases) to 1. Thus for
,
Furthermore, it is obvious that ☐
The next two lemmas are the properties of fuzzy metric on .
Lemma 1. Let N be a fuzzy norm on , t-norm . If we definewhere and is the total gHukuhara difference [33], then M is a fuzzy metric on . We call it fuzzy metric induced by N. Proof. (1) For , we have
(2) for
.
if and only if
(3) For
,
(4) For
,
(5) Since is continuous, so is .
All the conditions of Definition 2 are satisfied, so M is a fuzzy metric on ☐
Lemma 2. Let be a fuzzy metric induced by fuzzy norm . Then and scalar :
- (i)
- (ii)
Proof. (1) For
,
(2) For
and scalar
, we have
The result was proved. ☐
Theorem 3. Let be a separable normed space. There exists a fuzzy normed space and a function with the following properties:
- (1)
, is the fuzzy metric on ;
- (2)
;
- (3)
.
Thus, is embedded into a fuzzy normed space by .
Proof. By embedding the theorem in [
28], there exists an embedding function
such that
and
j is an isometrical and isomorphic function. We can take
, and
. Let
and
be the fuzzy metrics induced by
d and
, respectively, in the same style. Then for
,
(2) and (3) are obvious. ☐
Theorem 4. Let converge to A in the sense of ; is a fuzzy metric induced by ; then for Proof. By Theorem 3, there exists an embedding function
j such that
Since
,
. Thus by Theorem 2, for
,
Furthermore, by Theorem 3, for
The result has been proved. ☐
Theorem 5. Let M be a fuzzy metric induced by fuzzy norm N. Then for any Proof. By Theorem 1, Definition 3 (6), we have
The result has been proved. ☐
The following theorem gives the separability of the fuzzy metric space for sets.
Theorem 6. is a separable fuzzy metric space.
Proof. Since is a separable space. Let D be the countable dense subset with respect to Hausdorff distance . Then by Theorem 4, D is also the countable dense subset with respect to fuzzy metric ; the result has been established. ☐
Remark 2. From the proof of Theorems 2, 4 and 6, we can easily know that if , then for ,
4. Laws of Large Numbers in Fuzzy Metric
In this section, we shall give the convergence theorems for set-valued random variables in the sense of , which is induced by the Hausdorff metric . Firstly we introduce the Shapley–Folkman inequality for set-valued random variables in the sense of fuzzy metric , which will be used later.
Lemma 3. (cf. [34]) , thenwhere p is the dimension of . Then we have the Shapley–Folkman inequality for set-valued random variables in fuzzy metric space.
Theorem 7. Let . is a fuzzy metric induced by in the fuzzy metric space; thenfor any , where p is the dimension of . Proof. Therefore, for fixed
n, there exists
, such that
Furthermore by remark II, for
,
The result has been proved. ☐
Theorem 8. Let be a sequence of independent and identically distributed (i.i.d.) set-valued random variables. Then in the metric , we have the following convergence:that is, for Proof. Step 1. Let
be independent and identically distributed set-valued random variables and
be the isometry provided by Theorem 3. Then
are i.i.d.
-valued random elements and are integrable. By a standard SLLN in Banach space (see [
35]), it follows that
It follows from the embedding theorem (
j is isometric isomorphic mapping) that
Step 2. Consider the general case. Let
; then
is i.i.d. compact convex set. It follows from step 1 that
By Theorem 5, for
, we have
Finally, from the triangle inequality (Definition 2 (4)), it follows that
According to (2) and (3), the right values tend to 1. Then we have
The proof is complete. ☐
The sequence is said to be tight if for every there is a compact subset of such that for all .
From the definition of tightness, we can have the following lemma.
Lemma 4. Let be tight and j be the embedding function in Theorem 3; then is also tight.
Proof. Since
is tight, by the definition of tightness for set-valued sequence, we know that for every
there exists a compact subset
of
with respect to the metric
such that
for all
. Since
j is isometric isomorphic mapping,
is also a compact subset of
. We have
That means is tight. ☐
Theorem 9. Let be tight and independent set-valued random variables such that for all k where . Then in the metric , we have the following convergence:that is Proof. Step 1. Let
be independent set-valued random variables and
for all
k, and
be the isometry provided by Theorem 3. Then
are independent
-valued random elements and
By Lemma 4, we know that
in
is tight. By a standard SLLN in Banach space (cf. [
36], Theorem 2), it follows that
Since
then by (4) and Theorem 2, for
,
It follows from the embedding theorem (
j is isometric isomorphic mapping) that
Step 2. Consider the general case. Let
, so
is independent and satisfies
It follows from step 1 that
and by Theorem 7 we can have
Finally, from the triangle inequality (Definition 2 (4)), it follows that
According to (5) and (6), the right terms above tend to 1 when
. Then we have
The proof is complete. ☐
Next, we shall give two examples.
Example 1. In order to provide a more intuitive understanding of fuzzy metric, we give a practical example. Compare the close degree of return rate between stock and stock on a certain day, and measure it by fuzzy metric. As the stock price is changing in a day, we select three time points to record and give their return rates as follows:so . Take . For , for , . We can say that at the scale , and are extremely close. But at the scale , the degree of closeness is only . Example 2. We can use an interval-valued to describe the price of a stock in a day, where a and b are the minimal price and maximal price, respectively. Assume we get the interval-valued data ; they satisfy the conditions of Theorem 8; then at different level t(t can be thought of as a different evaluation scale), we consider the distance between the average and the population mean. Take Then by Theorem 8, we can get the convergence.