1. Introduction
In order to simplify the notations, the membership function
of a fuzzy set
is identified with
by simply writing
. Let
and
be two fuzzy sets in
, and let ⊙ denote any one of the arithmetic operations
between
and
. According to the extension principle, the membership function of
is defined by
for all
, where the arithmetic operations
correspond to the arithmetic operations
. The case of
should avoid the division of
for
.
In general, we can consider the t-norm instead of the minimum function by referring to Dubois and Prade [
1] and Weber [
2]. For more detailed properties, we can refer to the monographs by Dubois and Prade [
3] and Klir and Yuan [
4]. In this paper, we used the general function to propose the arithmetic operations of fuzzy sets, and we present the compatibility with the conventional definition using the minimum functions. We can also refer to Gebhardt [
5], Fullér and Keresztfalvi [
6], Mesiar [
7], Ralescu [
8], and Yager [
9] and Wu [
10] for the arithmetic operations of fuzzy sets based on the extension principle.
The generalization of Zadeh’s extension principle in (
1) can also be used to set up the arithmetic operations without using the minimum function. Coroianua and Fuller [
11,
12] used the so-called joint probability distribution to generalize the extension principle (
1), which is given by
for all
, where
is a joint probability distribution satisfying
Wu [
10] considered a general function
by defining the arithmetic as
where
does not need to satisfy some extra conditions. The main difference between (
2) and (
4) is that the domains of the joint probabilitydistribution
and function
are different. We can also refer to Coroianua and Fuller [
11] for the comparison between (
2) and (
4). Although
in (
4) is a general function, some sufficient conditions regarding
are still needed to obtain some desired properties. Therefore, the second motivation of this paper was to propose the concept of compatibility. We shall say that the function
is compatible with the arithmetic operations of
-level sets when the following equality:
is satisfied for each
. The sufficient conditions imposed upon the function
will be studied to guarantee the compatibility. Under the general function
, the associativity of the arithmetic operations is also an important issue. Therefore, many rules regarding the associativity were also studied.
There is some other interesting arithmetic of fuzzy numbers, which will be shown below. Holčapek, Škorupová, and Štěpnička [
13,
14] proposed the arithmetic of extensional fuzzy numbers based on a similarity relation
such that
S satisfies some required conditions. On the other hand, based on the concept of the extensional hull, given a fixed real number
, the so-called extensional fuzzy number generated by
x and a similarity relation
S is a fuzzy set
in
with membership degree
Given any two extensional fuzzy numbers
and
, the addition
and multiplication
are defined by
where
S is assumed to be the so-called separated similarity relation for the purpose of well-defined arithmetic. In general, based on a system
of so-called nested similarity relations, the addition
and multiplication
are defined by
Esmi et al. [
15] and Pedro et al. [
16] used the extension principle in (
3) to study the fuzzy differential equations. They considered the interactivity between fuzzy numbers. Let
be a fuzzy set in
. Given any fuzzy numbers
and
, we say that
is a joint probability distribution of
and
when
We say that
and
are non-interactive when
Otherwise, they are called interactive. The disadvantage is that the non-interactivity depends on their joint probability distributions. We cannot just say that
and
are non-interactive without considering the role of the joint probability distribution. Let ⊙ denote any one of the arithmetic operations
between fuzzy numbers
and
along with a joint probability distribution
. The membership function of
is defined by
for all
, where the case of
should avoid the division of
for
.
The arithmetic of fuzzy intervals is an important issue. Wu [
17] considered the form of expression in the decomposition theorem to study the arithmetic of fuzzy intervals. Wu [
18] also used the form of expression in the decomposition theorem to study the different types of binary operations of fuzzy sets, which were also applied to study the difference of fuzzy intervals and covered the so-called generalized differences proposed by Bede and Stefanini [
19] and Gomes and Barros [
20] as the special cases. The fuzzy axiom of choice, the fuzzy Zorn’s lemma, and the fuzzy Hausdorff maximal principle studied by Zulqarnian et al. [
21] were also based on normal fuzzy sets. It is also possible to extend those results based on the non-normal fuzzy sets.
The fuzzy sets considered in Wu [
17,
18] were implicitly assumed to be normal. Without using the form of expression in the decomposition theorem, in this paper, we shall use the extension principle based on a general function rather than the t-norm to study the arithmetic of non-normal fuzzy intervals. In this case, the concept of compatibility with
-level sets can be proposed and the equivalence with conventional arithmetic operations using the minimum function can also be established.
In
Section 2, the concept and basic properties of non-normal fuzzy sets will be presented, and the arithmetic operations of non-normal fuzzy sets will be studied using the extension principle based on the general functions. In
Section 3, we shall propose the concept of compatibility with the
-level sets, which can cover the conventional case using the minimum functions as the special case.
2. Arithmetic Operations of Fuzzy Sets
Let
be a fuzzy set in
. Recall that a fuzzy set
in a universal set
U is called normal when there exists
satisfying
. For
, the
-level set of
is denoted and defined by
The support of a fuzzy set
is the crisp set defined by
The 0-level set is defined to be the topological closure of the support of , i.e., . We write to denote the range of the membership function of . In general, we have . The following result is very useful.
Proposition 1. Let be a fuzzy set in with membership function . Define and Then, for all and for all . Moreover, we have and The interval is called an interval range of .
We considered three arithmetic operations
and ⊠ between any two fuzzy sets
and
in
. The extension principle says that the membership functions are given by
for all
, where the arithmetic operations
correspond to the arithmetic operations
. The case of division was not considered in this paper, since it can be similarly obtained.
Instead of the minimum function, we can consider a general function
defined on
. In this case, the membership functions are defined by
In general, the arithmetic operations are defined below.
Definition 1. Given any fuzzy sets in and a function defined on the product set , regarding the operations for , the membership function of is defined bywhere the operations for correspond to the operations for . When the function
is taken to be the minimum function given by
the membership function of
is given by
where
for
can refer to (
7), (
8), and (
9).
We can also insert the parentheses into the expression . The following example shows the way of inserting parentheses.
Example 1. Given fuzzy sets in , we can consider the membership functions ofandgiven byandrespectively. It is clear that . Sincethe fuzzy set means the following form: Example 2. We present an example from mathematical finance. The well-known Black–Scholes formula (see Black and Scholes [22]) for the European call option on a stock is described as follows. Let the function f be given by the formula:where s denotes the stock price, t denotes the time, K denotes the strike price, r denotes the interest rate, σ denotes the volatility, and N stands for the cumulative distribution function of a standard normal random variable . The quantities and are given by Let T be the expiry date, and let denote the price of a European call option at time . Then, we havewhere denotes the stock price at time t. On the other hand, the price of a European put option at time t with the same expiry date T and strike price K can be obtained by the following put–call parity relationship (see Musiela and Rutkowski [23]): Under the considerations of the fuzzy interest rate , fuzzy volatility , and fuzzy stock price , we can obtain the fuzzy price of a European call option at time t according to (12) and the extension principle. Therefore, the membership function of is given by According to the put–call parity relationship in (13), we can also study the fuzzy price of a European put option at time t. Let Then, we can obtain the fuzzy price of a European put option at time t in which the membership function of is given by Let
be fuzzy sets in
, and let
. From Proposition 1, we see that
for all
and
for all
, where the interval range
is given by
Let . Then, is also an interval of the form or for some . For , we see that for all .
Let
, and let
be the interval range of
. We also write
to denote the range of the membership function of
for
. The supremum of the range
of the membership function of
is given by
Therefore, the definition of interval range says
Proposition 2. Let be fuzzy sets in , and let with interval range . Suppose that the function satisfies the following condition: We also assumed that the supremum is obtained for . Then, the following supremum:is obtained. Moreover, we havewhere In particular, suppose thatThen, we have Proof. Since the supremum
is obtained for
, we have
for some
and for all
. It is also clear that
On the other hand, since
for all
, from (
15), again, we also have
which proves
We take
. Then, we have
and
Therefore, we obtain
. From (
15), we conclude that the supremum
is obtained at
. From (
16), it follows that
. This completes the proof. □
3. Compatibility
Let
be subsets of
. We write
where the arithmetic operations
for
.
Given any fuzzy sets
in
, let
be defined in Definition 1. For any
it is clear that the
-level sets
and
are nonempty for
. Therefore, we propose the following definition.
Definition 2. Given any fuzzy sets in , we considered the arithmetic operations , which correspond to the arithmetic operations for :
The function is said to be compatible with the arithmetic operations of α-level sets when the following equality is satisfied: The function is said to be strongly compatible with the arithmetic operations of α-level sets when the following equality is satisfied:
The purpose of this paper was to present some sufficient conditions such that the compatibility with the arithmetic operations of -level sets can be satisfied.
Recall that the real-valued function is upper semi-continuous on if and only if the set is a closed set in for each . Especially, if is a fuzzy set in such that its membership function is upper semi-continuous on , then each -level set is a closed subset of for .
Lemma 1 (Royden [
24] p. 161)).
Let K be a closed and bounded subset of , and let f be a real-valued function defined on . Suppose that f is upper semi-continuous on . Then, f assumes its maximum on K; that is, the supremum is obtained in the following sense: Theorem 1. Given any fuzzy sets in , we considered the arithmetic operations , which correspond to the arithmetic operations for . Then, we have the following properties:
- (i)
For any with , we assumed that the function satisfies the following condition: Then, the following inclusion:holds true for all . - (ii)
Suppose that the membership functions of are upper semi-continuous for all . We also assumed that the function satisfies the following conditions:
given any with , Given any with ,for any with .
Then, the following equality:holds true for all with . We further assumed that the supports are bounded for all . Then, the following equality:regarding the 0-level sets holds true.
Proof. To prove Part (i), given any
with
, we have
and
for all
. Given any
there exist
for all
satisfying
Using the assumption (
20) of
, we also have
Therefore, we obtain the following inclusion:
for all
with
.
Next, we considered the 0-level sets. For
, given any
there exist
for all
satisfying
For each fixed
i, since
the concept of closure says that there exists a sequence
satisfying
We considered a function
defined by
where the binary operations
for
. Then, it is clear that
is continuous. We define
Using (
26) and the continuity of
, we obtain
Given any
with
for
and any
with
, let
Then, we have
and
for
. From (
14), we also see
and
for all
, i.e.,
. The assumption (
20) of
says
Therefore, the following statement holds true:
Now, we have
which also says
From (
27), we obtain
which shows the following inclusion:
Therefore, we obtain the desired inclusion.
Proving Part (ii) means proving another direction of inclusion. Now, we further assumed that the membership functions of
are upper semi-continuous for all
. In other words, the nonempty
-level sets
are closed sets in
for all
and
. Given any
with
and any
we have
Since
is a finite number, we see that
is a bounded set in
. We also see that the function
is continuous on
. Since the singleton set
is a closed set in
, the continuity of
says that the inverse image
of
is also a closed set in
. This says that
F is a bounded and closed set in
. Next, we want to claim that the function
is upper semi-continuous. In other words, we want to show that
is a closed set in
for any
. We considered the different cases as follows:
Suppose that
. Then, we have
which is a closed set in
.
Suppose that
. Then, we have
which is also a closed set in
.
Suppose that
with
, i.e.,
for all
. Then, we have
which is a closed set in
, since
are closed sets in
for all
.
Suppose that
with
. Then, we have
for some
i, i.e.,
. By referring to (
14), it follows that
for all
. Therefore, using the assumption (
22), we obtain
This shows
which is also a closed set in
.
Suppose that
with
. Then, we have
which is a closed set in
.
The above cases conclude that the function
is indeed upper semi-continuous. Lemma 1 says that the function
f assumes the maximum on the set
F. Therefore, using (
29), we have
Therefore, there exists
satisfying
and
Using the assumption (
21), we obtain
, which says
for all
. Therefore, we obtain
which shows the following inclusion:
for all
with
. Using Part (i), we obtain the desired equality (
23).
Considering the 0-level sets, for
, we further assumed that the supports
are bounded for all
. Suppose that
for
and
. Since
is an interval beginning from 0, using the denseness of
, there exists
with
satisfying
Using the assumption (
21), we have
for all
, which says that the following statement holds true:
Now, considering the 0-level set, we have
Therefore, there exists a sequence
in the following set:
satisfying
Using the above arguments by referring to (
30), we can obtain
Therefore, there exist
satisfying
and
Using (
31), we have
for all
, which shows that the sequence
is in the support
for all
. Since each
is bounded for
, it follows that
is also a bounded sequence. Therefore, there exists a convergent subsequence
of
. In other words, we have
which also says
for all
. Let
Then, we see that
is a subsequence of
, i.e.,
Therefore, we obtain the following inclusion:
Using Part (i), we obtain the desired equality (
24), and the proof is complete. □
Theorem 2. Given any fuzzy sets in , we considered the arithmetic operations , which correspond to the arithmetic operations for . Suppose that the function satisfies the following conditions:
Given any with , Given any with ,for any with .
Then, we have the following properties:
- (i)
Suppose that the membership functions of are upper semi-continuous for all . Then, the function is compatible with arithmetic operations of α-level sets. In other words, given any with , we have In particular, if are normal, the equality (
32)
holds true for all . - (ii)
Suppose that the membership functions of are upper semi-continuous and that the supports are bounded for all . Then, the function is strongly compatible with the arithmetic operations of α-level sets. In other words, the equality (
32)
holds true for all . In particular, if are normal, the equality (
32)
holds true for all .
Proof. To prove Part (i), the equality (
32) follows immediately from Part (ii) of Theorem 1. In particular, if each
is assumed to be normal for
, then we have
for all
, which also says
. Part (ii) can be easily realized from Part (ii) of Theorem 1 and Part (i) of this theorem. This completes the proof. □
Corollary 1. Given any fuzzy sets in , we considered the arithmetic operations , which correspond to the arithmetic operations for . Then, we have the following properties:
- (i)
Suppose that the membership functions of are upper semi-continuous for all . Then, given any with , we have In particular, if are normal, the equality (
33)
holds true for all . - (ii)
Suppose that the membership functions of are upper semi-continuous and that the supports are bounded for all . Then, the equality (
33)
holds true for all . In particular, if are normal, the equality (
33)
holds true for all .
Proof. Since we considered the arithmetic operations
, this means that we take
which clearly satisfies all the assumptions of Theorem 2. Therefore, the desired results follow immediately from Theorem 2. □
Definition 3. We denote by the family of all fuzzy sets in such that each satisfies the following conditions:
The supremum is obtained, i.e., .
The membership function of is upper semi-continuous and quasi-concave on .
The 0-level set is a closed and bounded subset of .
Each is also called a fuzzy interval. If the fuzzy interval is normal and the one-level set is a singleton set , where , then is also called a fuzzy number with core value a.
If
is a fuzzy interval, then its 0-level set
is a closed and bounded subset of
. The conditions in Definition 3 says that each
-level set
is a bounded closed interval for
. It is also clear that
where
denotes the interval range of
and
is a bounded closed interval with endpoints
and
. The
-level set
can be interpreted as a bounded closed interval
with degree
, which explains the terminology of the fuzzy interval.
Proposition 3. Given any fuzzy intervals and with interval ranges and , respectively, let denote the interval range of for . Then, is also a fuzzy interval, and its α-level set is given byMore precisely, we havefor any . We further assumed that the suprema:are obtained. Then,is a closed interval. Proof. Given any , it is clear that the -level sets , , and are nonempty. Therefore, the desired results follow immediately from Corollary 1 and Proposition 2. This completes the proof. □
4. Conclusions
The arithmetic operations of non-normal fuzzy sets using the extension principle based on general functions were investigated in this paper. The membership function of arithmetic operation
is defined by
where the way of calculation
for
and
corresponds to the way of calculation for
for
and
. This kind of arithmetic operation generalizes the conventional one given by
where
for
.
The main issue of arithmetic operations is studying their
-level sets. Therefore, the concept of compatibility with
-level sets is proposed by saying that the function
is (strongly) compatible with the arithmetic operations of
-level sets when
It is clear that the minimum function:
considered in the conventional case is compatible with arithmetic operations of
-level sets.
Theorems 1 and 2 present the sufficient conditions to guarantee the compatibility with the arithmetic operations of
-level sets. This means that Theorems 1 and 2 are the general situation. Therefore, Corollary 1 and Proposition 3, which are the conventional cases, are the special cases of Theorems 1 and 2. This was the main purpose of this paper: to generalize the conventional cases. In other words, from some other functions
that can satisfy the sufficient conditions, the desired results can be obtained as the conventional cases. The main focus was on the functions
and the non-normal fuzzy sets, rather than the t-norm and the normal fuzzy sets. As we can see in Part (i) of Theorem 2, the equality (
32) holds true for non-normal fuzzy sets. The case of normal fuzzy sets is just the special case of (
32). Therefore, Theorems 1 and 2 indeed generalize the conventional cases. The limitation of Theorems 1 and 2 is checking the assumptions of general function
. Since those assumptions are satisfied for the conventional cases, as shown in Corollary 1 and Proposition 3, this also means that those assumptions are not too strong to be used in real applications.
The interval ranges of non-normal fuzzy sets comprise an important tool to handle the arithmetic of non-normal fuzzy sets. The future research will focus on the applications by using non-normal fuzzy sets and will solve the difficulty caused by the different forms of the interval ranges of non-normal fuzzy sets.