Abstract
The arithmetic operations of fuzzy sets are completely different from the arithmetic operations of vectors of fuzzy sets. In this paper, the arithmetic operations of vectors of fuzzy intervals are studied by using the extension principle and a form of decomposition theorem. These two different methodologies lead to the different types of membership functions. We establish their equivalences under some mild conditions. On the other hand, the -level sets of addition, difference and scalar products of vectors of fuzzy intervals are also studied, which will be useful for the different usage in applications.
1. Introduction
Let and be two fuzzy sets in with the membership functions and , respectively. The arithmetic operations , , and are based on the extension principle. More precisely, the membership functions are given by
for all . In this paper, we consider the vectors of fuzzy sets in . The purpose is to study the addition, difference and scalar products of vectors of fuzzy sets.
Suppose that and consist of fuzzy sets in given by
where and are fuzzy sets in for . Then, we study the addition , the difference and the scalar product .
The addition , the difference and multiplication regarding the components can be realized as shown above. Let and be the membership functions of and , respectively, 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 . More detailed properties can refer to the monographs of Dubois and Prade [1] and Klir and Yuan [2]. In general, we can consider the t-norms instead of minimum functions by referring to Bede and Stefanini [3], Dubois and Prade [4], Gebhardt [5], Gomes and Barros [6], Fullér and Keresztfalvi [7], Mesiar [8], Ralescu [9], Weber [10], Wu [11,12,13] and Yager [14]. More precisely, the membership function of is given by
for all , where t is a t-norm that is a function from into satisfying four axioms. It is well-known that the minimum function min is a t-norm. In this paper, we consider the general aggregation function rather than using t-norms. In this case, the membership function of is given by
for all , where is an aggregation function from into without needing to satisfy some required conditions.
According to the arithmetic operations (1), the addition , the difference and the scalar product can be naturally defined as follows
We can see that the scalar product is a fuzzy set in . The membership function of can be realized below. Let for . The membership function of can be obtained from (1). Therefore, the membership function of is given by
where is an aggregation function from into . In particular, the extension principle says that the aggregation function is given by the minimum function. Therefore, the membership function of is given by
We can see that and are still vectors of fuzzy sets. However, their membership functions cannot be obtained directly from (1). The main purpose of this paper is to propose two methodologies to define the membership functions of and . Those methodologies can also be used to define the membership function of the scalar product .
Following the conventional way, we can use the extension principle to define the arithmetic operations of vectors of fuzzy sets. In this paper, we consider the general aggregation functions rather than using t-norms. We should mention that the decomposition theorem is a well-known result in fuzzy sets theory. Alternative, we also use the form of decomposition theorem to define the arithmetic operations of vectors of fuzzy intervals. These two methodologies can lead to the different types of membership functions. In this paper, we establish the equivalences between using the extension principle and the form of decomposition theorem under some mild conditions.
In Section 2, the concept and basic properties of non-normal fuzzy sets are presented. In Section 3, the arithmetic operations of vectors of fuzzy sets are presented using the extension principle based on the general aggregation functions. In Section 4, the arithmetic operations of vectors of fuzzy sets are presented using the form of decomposition theorem. In Section 5, many types of difference of vectors of fuzzy sets are proposed using the extension principle and the form of decomposition theorem, and their -level sets are studied. Their equivalences are also established under some mild conditions. In Section 6, we study the addition of vectors of fuzzy sets following the same theme from Section 5. In Section 7, the scalar product of vectors of fuzzy sets are proposed, and their -level sets are also studied.
2. Non-Normal Fuzzy Sets
Let be a fuzzy set in with membership function . For , the -level set of is denoted and defined by
We remark that the -level set can be an empty set when is larger than the supremum of the membership function . This ambiguity will be clarified in this section. On the other hand, the support of a fuzzy set is the crisp set defined by
The 0-level set is defined to be the closure of the support of , i.e., .
The range of membership function is denoted by that is a subset of . We see that the range can be a proper subset of with . For example, the range can be some disjoint union of subintervals of .
Example 1.
The membership function of a trapezoidal-like fuzzy number is given by
It is clear to see that
Notice that if , we still can consider the -level set . Since , it is possible that the -level set can be an empty set for some . Therefore, when we study the properties that deal with more than two fuzzy sets, we cannot simply present the properties by saying that they hold true for each , since some of the -level sets can be empty. In this case, we need to carefully treat the ranges of membership functions.
Example 2.
Continuing from Example 1, we see that . However, we still have the -level set . It is clear to see that , where .
Let be a real-valued function defined on , and let S be a subset of . Recall that the supremum is attained if and only if there exists such that for all with . Equivalently, the supremum is attained if and only if
Define . If , then . If the supremum is not attained, then . For example, assume that
It is clear to see that . In this case, the supremum is not attained. However, we have . In this case, the 1-level set , since .
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 presented in Proposition 1 is also called an interval range of . We see that the interval range contains the actual range . The role of interval range can be used to say for all and for all . We also remark that and in general, since the range can be some disjoint union of subintervals of .
Example 3.
Continuing from Example 1, recall that . We also see that . Proposition 1 says that . It is clear to see that for all and for all .
Therefore, the interval plays an important role for considering the -level sets. In other words, the range is not helpful for identifying the -level sets.
Recall that is called a normal fuzzy set in if and only if there exists such that . In this case, we have . However, the range is not necessarily equal to even though is normal.
Let be a normal fuzzy set in . The well-known decomposition theorem says that the membership function can be expressed as
where is the characteristic function of the -level set . If is not normal, then we can similarly obtain the following form.
Theorem 1. (Decomposition Theorem)
Let be a fuzzy set in . Then the membership function can be expressed as
where is given in (3).
3. Arithmetics Using the Extension Principle
The generalized extension principle for non-normal fuzzy sets has been extensively studied in Wu [15]. In this paper, we use the extension principle to study the arithmetics of a vector of fuzzy intervals.
We denote by the family of all fuzzy sets in such that each satisfies the following conditions.
- The membership function is upper semi-continuous and quasi-concave on .
- The 0-level set is a compact subset of ; that is, a closed and bounded subset of .
Each is also called a fuzzy interval. If the fuzzy interval is normal and the 1-level set is a singleton set , where , then is also called a fuzzy number with core value a. It is well-known that the -level sets of fuzzy interval are all closed intervals denoted by for , which can be regarded as a closed interval with degree . This is the reason why we call as a fuzzy interval.
Example 4.
The membership function of a trapezoidal fuzzy interval is given by
which is denoted by . It is clear to see that
Proposition 1 says that the interval range is given by
If , then the α-level set . For , the α-level set is given by
Let and be two vectors in . Then, the arithmetics of vectors and are given by
Let and be two vectors of fuzzy intervals given by
Based on the extension principle (abbreviated as EP), we study the arithmetics of and by considering the scalar product , the addition and the difference . Given the aggregation function , the membership functions are defined below.
- For each , the membership function of the scalar product is given by
- For each and for the operation corresponding to the operation , the membership function of is given by
If the aggregation function is taken to be the minimum function, then the above arithmetics coincide with the extension principle.
Given any fuzzy intervals and in , let
and
From Proposition 1, the interval ranges of and of are given by
and
We also write to denote the ranges of membership functions for , and write to denote the ranges of membership functions for . Let
Example 5.
Continuing from Example 4, we consider the following trapezoidal fuzzy intervals
and
Then, we have
and the interval ranges are given by
and
From (10), by taking the aggregation function as the minimum function, we have
We denote by and the interval ranges of and , respectively, where and depend on and . The supremum of range of membership function is given by
We can similarly obtain
Therefore, the definition of interval range says that
and
Proposition 1 says that
and
Example 6.
Continuing from Example 5, we take the aggregation function as the minimum function. The membership function of scalar product is given by
and it is a continuous function. Therefore, the supremum
is attained. This says that the interval range of scalar product is given
By considering the α-level sets, we also see that
and
The membership function of addition is given by
The interval range of addition is given by
By considering the α-level sets, we also see that
and
The membership function of difference is given by
The interval range of addition is given
By considering the α-level sets, we also see that
and
For further discussion, we provide a useful lemma.
Lemma 1.
(Royden [16], p. 161) Let X be a topological space, and let K be a compact subset of X. Let f be a real-valued function defined on X. Then the following statements hold true.
- (i)
- If f is upper semi-continuous, then f assumes its maximum on a compact subset of X; that is, the supremum is attained in the following sense:
- (ii)
- If f is lower semi-continuous, then f assumes its minimum on a compact subset of X; that is, the infimum is attained in the following sense:
Proposition 2.
Suppose that the aggregation function is given by
Let
Then, the following statements hold true.
- (i)
- We have
- (ii)
- The supremum is attained if and only if the supremum is attained, and the supremum is attained if and only if the supremum is attained.
- (iii)
- We have
Proof.
It suffices to prove the case of , since the case of can be similarly obtained. From (11), we have
On the other hand, from (11) again, we also have
which proves part (i).
Suppose that the supremum is attained. From (11), there exists such that
Since the set is closed and bounded, i.e., a compact set, and the functions and are upper semi-continuous, Lemma 1 says that the supremum in (13) is attained. In other words, there exists such that
For convenience, we write , and for . Then, from (13) and (14), we have
and we can say that for some . Part (i) also says that for some . Then, using (15), we have
which says that the supremum is attained. Using (8) and (9), we obtain is a closed interval, which also says that . Therefore, we conclude that the supremum is also attained.
On the other hand, suppose that the supremum is attained. Then, we have
and
which also says that the supremum is attained; i.e., there exists such that . By referring to (12), there exists such that its -component is and
where for . In this case, we have , which says that the supremum is attained, which proves part (ii). Finally, part (iii) follows immediately from parts (i) and (ii). This completes the proof. □
4. Arithmetics Using the Form of Decomposition Theorem
The differentiation and integrals of fuzzy-number-valued functions using the form of decomposition theorem have been studied in Wu [17]. In this paper, we use the form of decomposition theorem to study the arithmetics of vector of fuzzy intervals.
Let and be two vectors of fuzzy intervals with components and , respectively, for . Let
Then is not empty, since and are intervals with left end-point 0 for . For each , the -level sets of and are nonempty and denoted by
We write
We also write
and
In order to define the difference , we consider the family that is formed by applying the operation to the -level sets and for , where each is a subset of . In this paper, we study three different families described below.
- We taketo define .
- We takewhere are bounded closed intervals given byfor to define .
- We takewhere are bounded closed intervals given byfor to define .
For , based on the form of decomposition theorem, the membership function of is defined by
Example 7.
Continuing from Example 5, we have
From (5), we have
Then we have
and
We consider three families
given below.
- We takefor all . The membership function of is given by
- We takewhere and are bounded closed intervals given bywhich says that is a singleton set for all . Similarly, we can obtainfor all . Therefore, we obtainfor all , which is a singleton set in . The membership function of is given by
- We takewhere and are bounded closed intervals given byandTherefore, we obtainfor all , which is a singleton set in . The membership function of is equal to membership function of .
In order to define the addition , we consider the family that is formed by applying the operation to the -level sets and for , where each is a subset of . In this paper, we study three different families described below.
- We taketo define .
- We takewhere are bounded closed intervals given byfor to define .
- We takewhere are bounded closed intervals given byfor to define .
For , based on the form of decomposition theorem, the membership function of is defined by
Example 8.
Continuing from Examples 5 and 7, we consider three families
given below.
- We takefor all . The membership function of is given by
- We takewhere and are bounded closed intervals given byfor all . We also obtainfor all . Now, we haveandTherefore, we obtainfor all . The membership function of is equal to the membership function of
- We takewhere and are bounded closed intervals given byandTherefore, we obtainfor all . The membership function of is equal to membership function of .
In order to define the scalar product of and , we consider the family that is formed by applying the operation to the -level sets and for , where each is a subset of . In this paper, we study three different families described below.
- We taketo define the scalar product .
- We takewhere are bounded closed intervals given byto define the scalar product .
- We taketo define the scalar product .
For , based on the form of decomposition theorem, the membership function of is defined by
Example 9.
Continuing from Examples 5 and 7, we consider three families
given below.
- We takefor all . The membership function of is given by
- We takewhere is a bounded closed interval given byfor all . Therefore, we obtainThe membership function of is equal to the membership function of .
- We takeThen, we obtainTherefore, the membership function of is equal to the membership function of .
We denote by and the interval ranges of membership functions and for , respectively, where and depends on and . We consider the supremum of range of membership function as follows:
We can similarly obtain
Therefore, the definition of interval ranges says that
and
Proposition 1 also says that
and
Proposition 3.
Let and be fuzzy intervals. Suppose that the supremum in (16) is attained. Then
Proof.
More detailed properties will be studied separately in the sequel.
Example 10.
Continuing from Examples 7–9, we consider the interval ranges , and of , and , respectively. Recall that . From (23), we see that . Proposition 3 says that
Therefore, it follows that
and
5. Difference of Vectors of Fuzzy Intervals
Let and be two vectors of fuzzy intervals with components and , respectively, for . Here we study the -level set of that is obtained from the extension principle, and the -level sets of for that are obtained from the form of decomposition theorem.
5.1. Using the Extension Principle to Study the -Level Sets of
Given any aggregation function , recall that the membership function of difference is defined by
for any . Let be the interval range of . The -level set of for can be obtained by applying the results obtained in Wu [11] to the difference , which is shown below. For each with , we have
The 0-level set is given by
Moreover, for each , the -level sets are closed and bounded subsets of .
Now, the aggregation function is given by
Proposition 2 says that . Therefore, for each , we have , and for all . Now, for each with , using (27), we have
Regarding the components and , let be the interval range of . From Proposition 2, we can similarly obtain . For , we also have
The above results are summarized in the following theorem.
Theorem 2.
Let and be any fuzzy intervals. Suppose that the aggregation function is given by
Then, we have the following results.
- (i)
- Let be the interval range of for . Then, for each , we haveWe also have .
- (ii)
- Let be the interval range of . We haveFor each , we also haveand
Remark 1.
When and are taken to be fuzzy numbers instead of fuzzy intervals, it follows that
Therefore, Theorem 2 says that
for all .
Example 11.
Continuing from Examples 5 and 7, Theorem 2 says that
and
and
for . Moreover, we have for .
5.2. Using the Form of Decomposition Theorem to to Study the -Level Sets of
Let and be fuzzy intervals. The family is given by
We see that and for each and for . Now, for with , we have
Based on the form of decomposition theorem, the membership function of is given by
Let be the interval range of . The -level sets of for are presented below.
Proposition 4.
Suppose that the supremum is attained. Then and
for each .
Proof.
We first consider with . Given any , we see that by (31). Therefore, we obtain , which proves the inclusion .
For proving another direction of inclusion, it is clear to see that is a nested family. Given any , i.e., , let . Assume that . Let . According to the concept of supremum, there exists satisfying and , which implies . This also says that , since by the nestedness.
Now, we assume that . Since is an interval with left end-point 0, given any with , we can consider the sequence in satisfying with for all s. Since and are fuzzy intervals for , it is well-known that
Since
we can obtain
Since , we conclude that
Let . According to the concept of supremum, there exists satisfying and , which implies . This also says that by the nestedness for all s. Therefore, we conclude that . From (33), it follows that . Therefore, for with , we obtain
For the 0-level set, since from Proposition 3, by referring to (4), it is not difficult to show that
This completes the proof. □
Now, for and for with , we take
From (30), we see that
Let be obtained using the form of decomposition theorem based on the family that is defined in (34). Let be the interval range of . Suppose that the supremum is attained for each . Then each is a bounded and closed interval with left end-point 0 for . In this case, it is also clear to see that is a bounded and closed interval with left end-point 0; that is, the supremum is also attained. By referring to Proposition 4, we can similarly obtain and
for and , which also implies
for each . The above results are summarized below.
Theorem 3.
Let and be fuzzy intervals. Suppose that the family is given by
Let be the interval range of , and let be the interval range of for .
- (i)
- Suppose that the supremum is attained. Then andfor each .
- (ii)
- Suppose that the supremum is attained for each . Thenfor each and each , andfor each .
Remark 2.
Example 12.
Continuing from Example 7, part (i) of Theorem 3 says that
for . Moreover, we have for .
5.3. Using the Form of Decomposition Theorem to to Study the -Level Sets of
Let and be any fuzzy intervals. The family is given by
and
where are bounded closed intervals given by
for . Based on the form of decomposition theorem, the membership function of is given by
Let be the interval range of . Here we study the -level sets of for .
For , we write
It is clear to see that is a nested family. Since is a bounded interval with left end-point 0, using the nestedness, we can show that
From (37), we also see that
Using (38), we can also obtain
Suppose that the supremum is attained. By applying (39) to the argument in the proof of Proposition 4, we can show that and
for any .
Now, we consider the difference of components and for . Using the form of decomposition theorem, the membership function of is defined by
Let be the interval range of . We also study the -level sets of for . Suppose that the supremum is attained. Using the argument in the proof of Proposition 4 again, we can obtain and
for any .
In order to obtain the compact form of the -level sets, we propose a concept below.
Definition 1.
We say that is a canonical fuzzy interval if and only if is a fuzzy interval such that and are continuous with respect to α on .
Now, we assume that and are any canonical fuzzy intervals. Let
Then .
We also see that and are continuous functions on . Then, for with , we can obtain
The above results are summarized below.
Theorem 4.
Let and be any fuzzy intervals. Suppose that the family is given by
and
where are bounded closed intervals given by
for . Let be the interval range of , and let be the interval range of for .
- (i)
- Suppose that the supremum is attained. Then andfor each .
- (ii)
- Suppose that the supremum is attained for each . Thenfor each and each , andfor each .
Assume that and are canonical fuzzy intervals. Then, for , we have
that are bounded and closed intervals.
Example 13.
Continuing from Example 7 by referring to (20), part (i) of Theorem 4 says that
for . Moreover, we have for .
5.4. Using the Form of Decomposition Theorem to Study the -Level Sets of
Let and be any fuzzy intervals. The family is given by
and
where are bounded closed intervals given by
for . Based on the form of decomposition theorem, the membership functions of and for are given by
for any and
for any , respectively. Let and be the interval ranges of and , respectively, for . Herein we study the -level sets of for , and the -level sets of for . We first provide some useful lemmas.
Lemma 2.
Let I be a closed subinterval of given by for some . Let and be two bounded real-valued functions defined on I with for each . Suppose that the following conditions are satisfied:
- is an increasing function and is a decreasing function on I;
- and are left-continuous on .
Let for . For any fixed , the function
is upper semi-continuous on I.
Lemma 3.
Let I be a closed subinterval of given by for some . For each , let and be bounded real-valued functions defined on I with for each . Suppose that the following conditions are satisfied:
- are increasing function and are decreasing function on I for ;
- and are left-continuous on for .
Let for and for , and let . For any fixed , the following function
is upper semi-continuous on I.
Proof.
Lemma 2 says that the functions
are upper semi-continuous on I for . For , we define the sets
The upper semi-continuity of says that is a closed set for . For , we want to claim . Given any , it follows that and , i.e., and for , which also implies for . Therefore, we obtain the inclusion . On the other hand, suppose that for . It follows that and for ; i.e., and . Therefore, we obtain the equality , which also says that is a closed set, since each is a closed set for . For , it is clear to see that is a closed subinterval of . Therefore, we conclude that is indeed upper semi-continuous on I. This completes the proof. □
Now, we assume that the supremum is attained. Then is a bounded closed interval with by referring to Proposition 3. We also assume that and are canonical fuzzy intervals. Under these assumptions, we claim
Let
Then . We also see that and are continuous functions on . Using Lemmas 2 and 3, given any fixed , the functions
and
are upper semi-continuous on for .
Given any with , suppose that and for all with . Then for all . Since is a bounded closed interval, i.e., a compact set, and is upper semi-continuous on as described above, the supremum of the function is attained by Lemma 1. This says that
for some , which violates . Therefore, there exists with satisfying , which shows the following inclusion:
On the other hand, the inclusion
is obvious. This shows (40).
Suppose that the supremum is attained. Then is also a bounded closed interval. Therefore, we can similarly obtain and
The above results are summarized below.
Theorem 5.
Let and be canonical fuzzy intervals. Suppose that the family is given by
and
where are bounded closed intervals given by
for . Let be the interval range of , and let be the interval range of for .
- (i)
- Suppose that the supremum is attained. Then andfor each with , and the 0-level set
- (ii)
- Suppose that the supremum is attained for each . Thenfor each with and each . The 0-level set isMoreover, for , we havewhich are bounded and closed intervals.
Remark 3.
We remark that, in general, we cannot have the following equality:
When and are taken to be canonical fuzzy numbers instead of canonical fuzzy intervals, it follows that
Then, we can have the following equality:
Example 14.
Continuing from Example 7 by referring to (21), we have . Part (i) of Theorem 5 says that
for . Moreover, we have for .
5.5. The Equivalences and Fuzziness
Next, we present the equivalences between and in Theorems 2 and 3, respectively.
Theorem 6.
Let and be any fuzzy intervals. Suppose that and are obtained from Theorems 2 and 3, respectively. We also assume that the supremum is attained. Then
Moreover, for , we have
Proof.
From Propositions 3 and 2, we have . The equality (41) follows immediately Theorems 2 and 3, which also says that . This completes the proof. □
We are not able to study the equivalences among , and . However, we can study their fuzziness by considering the -level sets. The formal definition regarding the fuzziness is given below.
Definition 2.
Let and be two fuzzy intervals with interval ranges and , respectively. We say that is fuzzier than if and only if and for all with .
Suppose now that we plan to collect real number data in . Owing to the unexpected situation, we cannot exactly obtain the desired data. Instead, we can just obtain the fuzzy data that can be described by some suitable membership functions. Now, we have two ways to calculate the difference between and . One is based on the extension principle to obtain , and another one is based on the form of decomposition theorem to obtain for . We claim that is fuzzier than . In other words, we prefer to take , which has less fuzziness.
Let and be canonical fuzzy intervals, and let and be obtained from Theorems 4 and 5, respectively. Suppose that the supremum is attained. Then we have
For each with , we also have
and
Since the inclusion
is obvious, it follows that
which says that is fuzzier than .
On the other hand, from Theorems 6 and 4, we have
For each with , we also have
It follows that
which says that is fuzzier than . The above results are summarized below.
Theorem 7.
Let and be canonical fuzzy intervals. Suppose that , , and are obtained from Theorem 2, Theorem 3, Theorem 4 and Theorem 5, respectively. We also assume that the supremum is attained. Then
and
for each . In other words, is fuzzier than , and is fuzzier than .
Remark 4.
Theorem 7 says that, when and are taken to be canonical fuzzy intervals, we may prefer to pick that has less fuzziness in applications.
6. Addition of Vectors of Fuzzy Intervals
Let and be two vectors of fuzzy intervals with components and , respectively, for . Next we study the -level set of that is obtained from the extension principle, and the -level sets of for that are obtained from the form of decomposition theorem.
6.1. Using the Extension Principle to Study the -Level Sets of
Given any aggregation function , the membership function of addition is defined by
for any . Let be the interval range of . The -level set of for can be obtained by applying the results obtained in Wu [11] to the addition , which is shown below. For each with , we have
The 0-level set is given by
Moreover, for each , the -level sets are closed and bounded subsets of .
When the aggregation function is given by
Proposition 2 says that . Therefore, for each , we have , and for all . Now, for each with , using (42), we have
Regarding the components and , let be the interval range of . From Proposition 2, we can similarly obtain , and, for each , we also have
The above results are summarized in the following theorem.
Theorem 8.
Let and be fuzzy intervals. Suppose that the aggregation function is given by
Then, we have the following results.
- (i)
- Let be the interval range of for . For each , we haveWe also have .
- (ii)
- Let be the interval range of . We haveFor each , we also haveand
Example 15.
Continuing from Examples 5 and 7, part (ii) of Theorem 8 says that
and
and
for . Moreover, we have for .
6.2. Using the Form of Decomposition Theorem to Study the -Level Sets
Let and be fuzzy intervals. The family is given by
Since and for and , given any with , we have
Based on the form of decomposition theorem, the membership function of is given by
Let be the interval range of . Suppose that the supremum is attained. Using the similar argument in the proof of Proposition 4, we can obtain and the -level sets of are given by
for .
Now, for and for with , we take
Then, for , from (45), we see that
Let be obtained using the form of decomposition theorem based on the family
that is defined in (47). Let be the interval range of . Suppose that the supremum is attained for each . Then the supremum is also attained. For , we can similarly obtain and
which also implies
The above results are summarized below.
Theorem 9.
Let and be fuzzy intervals. Suppose that the family is given by
Let be the interval range of , and let be the interval range of for .
- (i)
- Suppose that the supremum is attained. Then andfor each . Moreover, we havewhere is obtained from Theorem 8.
- (ii)
- Suppose that the supremum is attained for each . Thenfor each and each , andfor each .
Next, we study the addition by considering a family that has the same form of Theorem 4. We first need a useful property given below.
Lemma 4.
Let be a fuzzy interval with interval range . Then the function is lower semi-continuous on , and the function is upper semi-continuous on .
Theorem 10.
Let and be fuzzy intervals. Suppose that the family is given by
and
where are bounded closed intervals given by
for . Then
If we further assume that the supremum is attained, then
Proof.
Let and . Then . Lemma 4 say that is lower semi-continuous on and is upper semi-continuous on . Then, for with , we can obtain
Next, we study the addition by considering a family that has the same form of Theorem 5. However, in this case, we need to consider the canonical fuzzy intervals rather than the fuzzy intervals.
Theorem 11.
Let and be canonical fuzzy intervals. Suppose that the family is given by
and
where are bounded closed intervals given by
for . Suppose that the supremum is attained. Then
Proof.
For each , it is clear to see that
Since the supremum is attained, for each with , using the similar argument of Theorem 5, we can obtain
This completes the proof. □
We remark that Theorem 11 needs to consider the canonical fuzzy intervals rather than the fuzzy intervals, and assume that the supremum is attained.
Remark 5.
When and are taken to be canonical fuzzy numbers instead of canonical fuzzy intervals, it follows that
which also says that the supremum is attained. Therefore, the above theorems are applicable.
Example 16.
Using Theorem 11 and Example 15, we see that
and
7. Scalar Product of Vectors of Fuzzy Intervals
In the sequel, we are going to use the extension principle by referring to (6) to study the scalar product , and use the form of decomposition theorem by referring to (22) to study the scalar product .
7.1. Using the Extension Principle
Given any aggregation function , the membership function of scalar product is defined by
for any . Let be the interval range of . The -level set of for can be obtained by applying the results obtained in Wu [11] to the scalar product , which is shown below. For each with , we have
The 0-level set is given by
Moreover, for each , the -level sets are closed and bounded subsets of .
Now, the aggregation function is given by
Proposition 2 says that . Therefore, for each , we have , and for all . Now, for each with , using (49), we have
where and are given in (18) and (19). For the 0-level set, from (50) and (4), it is not difficult to show that
Definition 3.
Let be a fuzzy set in with membership function . We say that is nonnegative when for each .
It is clear to see that a fuzzy interval is nonnegative if and only if for each . Suppose that and are nonnegative fuzzy intervals. Then
The above results are summarized in the following theorem.
Theorem 12.
Let and be any fuzzy intervals. Suppose that the aggregation function is given by
Example 17.
Continuing from Examples 5 and 7, Theorem 12 says that we need to calculate
and
In other words, given any fixed , we want to calculate
Since , the minimum is
and the maximum is
Therefore, Theorem 12 says that
for . Moreover, we have for .
7.2. Using the Form of Decomposition Theorem
Let and be fuzzy intervals. The family is given by
and
Since and for and , given any with , we have
Based on the form of decomposition theorem, the membership function of is given by
Let be the interval range of . Suppose that the supremum is attained. Using the similar argument in the proof of Proposition 4, we can obtain and the -level sets of is given by
for . The above results are summarized below.
Theorem 13.
Let and be any fuzzy intervals. Suppose that the family is given by
We also assume that the supremum is attained. Then , and, for , we have
When and are taken to be nonnegative fuzzy intervals, we have
Example 18.
By referring to Example 17, Theorems 12 and 13 say that
for . Moreover, we have for .
Next, we study the scalar product by considering a different family that has the similar form of Theorem 4. Recall that is a canonical fuzzy interval in a universal set U if and only if is a fuzzy interval such that and are continuous with respect to on .
Theorem 14.
Let and be canonical fuzzy intervals. Suppose that the family is given by
where is a bounded closed interval given by
We also assume that the supremum is attained. Then , and, for , we have
When and are taken to be nonnegative canonical fuzzy intervals, we have
Proof.
We define two functions and on as follows:
Then and are continuous on , since we consider the canonical fuzzy intervals. We also see that . Using the similar argument of Theorem 4, we can obtain , and, for , we also have
This completes the proof. □
Example 19.
Continuing from Examples 5 and 7, we can obtain
and
Using (51), we have
for . Moreover, we have for .
Theorem 15.
Let and be any canonical fuzzy intervals. Suppose that the family is given by
and
We also assume that the supremum is attained. Then , and, for , we have
When and are taken to be nonnegative canonical fuzzy intervals, we have
Proof.
Using the similar argument of Theorem 5, we can obtain . For , we also have
By referring to (52), we complete the proof. □
Example 20.
By referring to Example 19, Theorems 14 and 15 say that
for . Moreover, we have for .
7.3. The Equivalences and Fuzziness
Next, we present the equivalences among and for .
Theorem 16.
Let and be fuzzy intervals. Suppose that and are obtained from Theorems 12 and 13, respectively. We also assume that the supremum is attained. Then
Moreover, for , we have
Theorem 17.
Let and be canonical fuzzy intervals. Suppose that and are obtained from Theorems 14 and 15, respectively. We also assume that the supremum is attained. Then
Moreover, for , we have
Theorem 18.
Let and be nonnegative canonical fuzzy intervals. Suppose that , , and are obtained from Theorem 12, Theorem 13, Theorem 14 and Theorem 15, respectively. We also assume that the supremum is attained. Then
Moreover, for , we have
The equivalence between and cannot be guaranteed. The following theorem compares their fuzziness.
Theorem 19.
Let and be canonical fuzzy intervals. Suppose that and are obtained from Theorems 13 and 14, respectively. We also assume that the supremum is attained. Then and is fuzzier than .
Proof.
For with , it is clear to see that
and
From Theorems 16 and 17, we obtain
for each with , which says that is fuzzier than . This completes the proof. □
Funding
This research received no external funding.
Conflicts of Interest
The author declares no conflict of interest.
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