1. Introduction
The classical numerical system of real numbers is not efficient to express the vagueness, uncertainty and imprecision of real life. Fuzzy numbers and modal intervals are useful tools to get over those deficiencies.
The introduction of fuzzy sets by Zadeh [
1] was a novelty as they provide a graduation of the membership relation. When considering fuzzy numbers, the expression “to be an element of a set” makes no sense, whereas many expressions in which this membership relation is relativized do indeed make sense.
Fuzzy numbers can be considered from two different points of view: with their membership function or with their -cuts. The two ways of considering fuzzy numbers are equivalent, and, depending on the details we want to study, one can be better than the other. Among all types of fuzzy numbers, triangular and trapezoidal ones, whose names are derived from the shape obtained when their membership function is represented in the Cartesian plane, are the most commonly used. However, when we observe a fuzzy number from the point of view of its -cuts, what we are indeed getting is an intervalar point of view of the fuzzy number.
Fuzzy sets theory has evolved since its appearance in 1965. Nowadays, we can find applications of fuzzy sets in the most part of scientific disciplines, such as decision-making [
2,
3,
4], probability [
5], control theory [
6], medical sciences [
7], characterization of complex systems [
8], among others.
Modal intervals were introduced by Gardeñes [
9] and they implied a new treatment of interval analysis, providing new resources to solve problems and systematize their resolution, as well as to interpret correctly an intervalar calculus.
Fuzzy numbers and intervals are not far removed from each other. This is because a fuzzy number can be identified by its -cuts, which are intervals, and intervals can also be considered fuzzy numbers.
The set of intervals, in their classical point of view, has some deficiencies in the operative sense and also in the interpretation of the calculus. These deficiences remain in the set of fuzzy numbers. Modal intervals, which are an extension of classical intervals, solve some of the operative deficiencies (although the distributive property is neither satisfied), and they give, without ambiguity, the right semantical interpretations of the calculus. Moreover, modal intervals are a lattice with regard to the incluson relationship, while classical intervals are not.
The advantages that present modal intervals in front of classical intervals have motivated us to deepen the relationship between fuzzy sets theory and interval analysis theory using modal intervals. We are certain that, with this tool, we open new lines of research.
If the connection between intervals and fuzzy numbers is so obvious, why not use modal intervals when working with fuzzy numbers, especially if we take into account the fact that modal intervals are an efficient extension of classical intervals? This is what we want to do in this paper: we will focus on the modal interval point of view of the -cuts of trapezoidal fuzzy numbers.
Modal intervals have been used combined with fuzzy sets [
10,
11]. The study that we present is based on the set of trapezoidal fuzzy numbers, expanding this set using modal intervals. The new fuzzy numbers obtained by this extension are named 
modal interval trapezoidal fuzzy numbers.
In this new set of modal interval trapezoidal fuzzy numbers (MITFNs), we study the inclusion relationship and we prove that this relationship provides a lattice structure. At the same time, in the set of MITFNs, we define the dual operator, inherited from the dual operator of modal intervals. The dual operator is an internal operator in the set of MITFNs, and it gives us the possibility to solve problems that, until now, had no solution in the set of traditional fuzzy numbers.
The rest of this paper is organized as follows. In 
Section 2, some basic concepts related to modal intervals and to fuzzy numbers are given. In 
Section 3, we provide the main definitions of this work and we also study the lattice structure of MITFNs with regard to the inclusion relation. In 
Section 4, we define the modal extension of a real function, we study some properties of this extension and we present the semantic interpretability theorem. 
Section 4 also includes an example to show some advantages when working on MITFNs instead of working with trapezoidal fuzzy numbers in their traditional sense. The conclusions and future research are described in 
Section 5.
  3. Modal Interval Trapezoidal Fuzzy Numbers
Definition 1. (Modal interval trapezoidal fuzzy number)
Given  such that .
If, for any , we consider , then  is an MITFN which we represent by 
 The modal interval  that corresponds to  is the support of A, and the modal interval  that corresponds to  is the core of  Thus, .
We denote the set of MITFNs by  extending the expression  which denotes the set of modal intervals.
If 
 is an MITFN, we can define 
 as the trapezoidal fuzzy number (in its standard sense):
It is obvious that  and 
Definition 2.  (Modality of an MITFN)
Given  which is an MITFN, we define:
- A is a proper-improper MITFN (MITFN) if  is a proper interval and  is an improper interval. We denote it by ; 
- A is an improper-proper MITFN (MITFN) if  is an improper interval and  is a proper interval. We denote it by ; 
- A is a proper-proper MITFN (MITFN) if both the support and core of A are proper intervals. We denote it by ; 
- A is an improper-improper MITFN (MITFN) if both the support and core of A are improper intervals. We denote it by . 
 In general, an MITFN 
A will be:
      where 
 is a trapezoidal fuzzy number in its traditional sense; 
 is the interval modality of the support of 
 that is, the modality of 
; and 
 is the interval modality of the core of 
 that is, the modality of 
.
If 
A is an MITFN
, we will refer to 
A as a proper trapezoidal fuzzy number; while if 
A is an MITFN
, then we will refer to 
A as an improper trapezoidal fuzzy number [
10]. Notice that if 
A is an MITFN
, then 
A is a trapezoidal fuzzy number in its classical sense.
Proposition 1. Given  which is an MITFN or an MITFN, there exists a unique value  such that the α-cut  is a pointwise interval . Moreover, if  then  and if , then 
 Proof.  If A is an MITFN
, 
 and 
. As 
.We must impose:
          
          that is:
          
          which has a unique solution as 
Let 
 be the solution. If 
, then 
. If A is an MITFN
, then we have to prove that 
 is a proper interval. As 
 is a proper interval, then 
. As 
 is an improper interval, then 
          as 
  and
          
          but 
 is either 
 or 
, so it follows that:
          
          and then
          
          so
          
          which means that 
 is a proper interval.
If , then  and the demonstration would be equivalent.
Similar reasoning holds for the case in which A is an MITFN. ☐
 We will refer to 
 as the transition modality value of the MITFN, 
A. The pointwise interval 
 is the transition 
cut of 
A, and its graphical representation is shown in Figure 4. If 
 is an MITFN
 or an MITFN
 and 
 is the transition 
cut of 
A, then 
 is a modal interval triangular non-normalized fuzzy number and its modality is the same as the interval modality of the support of 
A, that is, if 
A is an MITFN
, then 
 will be a proper triangular fuzzy number; while, if 
A is an MITFN
, then 
 will be an improper triangular fuzzy number [
10].
Proposition 2. (Canonical characterization of an MITFN)
Let , then
- A is an MITFN; 
- A is an MITFN; 
- A is an MITFN; 
- A is an MITFN. 
 Proof.  - If A is an MITFN, then  is proper, that is,  and  is improper, that is,  - As Definition 1 establishes , in this case,  and  which means  , that is  and  - From  , it follows that    
- If A is an MITFN,  is an improper interval, that is,  and  is a proper interval, that is,  - As it must be that  and   so it follows that  , that is,  and  - From  , it follows that  
The proof of both case 3 and case 4 is trivial. ☐
 Proposition 3. (Interval modality of the expected interval)
If  is an MITFN, then the interval modality of the expected interval is the same as the interval modality of the support of  that is:  Proof.  The expected interval for a trapezoidal fuzzy number  is 
- If both  and  are proper intervals, then  and  so  and  is a proper interval.   
- If both  and  are improper intervals, then  and  so  and  is an improper interval.   
- If  is a proper interval and  is an improper one, then  and . As  it holds that  and  Thus,  is a proper interval.   
- If  is an improper interval and  is a proper interval, then  and . As  it holds that  and  Thus,  is an improper interval. 
		 ☐
 The graphical representation of the expected interval is shown in Figure 5.
It is possible to consider the membership function of an MITFN, but it is difficult to represent improper intervals in the Cartesian plane. However, a graphical visualization of the functions 
 and 
 is provided in 
Figure 2.
Next, we will define some operators on the set of MITFNs.
Dual operator. If 
  the dual operator on 
A, 
 is defined as:
      where 
 We will distinguish between the dual operator of an MITFN, which we will represent by  and the dual operator of an interval, which we will represent by 
If , then . Moreover,  and, consequently,  and , that is, if , then 
It is obvious that if A is an MITFN, then  is an MITFN; if A is an MITFN, then  is an MITFN; if A is an MITFN, then  is an MITFN; and if A is an MITFN, then  is an MITFN.
Proper and improper operators. If 
  we define the proper operator on 
 as 
 and the improper operator on 
A as 
. Using the canonical notation, if 
, then:
	  which coincides with 
, and
      
 Both 
 and 
 are quantified trapezoidal fuzzy numbers [
10].
  3.1. Graphical Representation of an MITFN in the Interval Plane
Trapezoidal fuzzy numbers, in their traditional sense, can be represented in Moore’s semiplane as decreasing segments in which the support is represented by ∘ and the core is represented by • [
30].
Moore’s semiplane is not detailed enough to represent an MITFN, and we must use the intervalar plane to represent them graphically. Using the same notation, that is, representing the support of an MITFN 
A by ∘ and representing its core by •, the segment with ends ∘ and • represents the MITFN 
A. The graphic representation of the four types of MITFNs, described in Definition 2, is shown in 
Figure 3.
The following graphical representation 
Figure 4 allows us to interpret the existence and uniqueness of the transition modality value easily, as well as the transition 
-cut of an MITFN
 or an MITFN
. 
Figure 5 is the graphic representation of the expected interval of an MITFN.
  3.2. The Lattice of MITFNs
We have defined the MITFN (Definition 1) using the 
-cuts, and we will now define the inclusion relation between two MITFNs using the modal interval inclusion of the 
-cuts, that is:
In the following proposition, we study the inclusion relation between two MITFNs A and B using the modalities of the support and the core of both A and B. There are 16 cases to be considered, but, using the properties of the modal interval duality, we can reduce those 16 cases to 10.
Lemma 1. Let , then:  Proof.   As , taking  and , it follows that  and .
  and .
As  and   Moreover, as   then  
Thus,  . ☐
 Proposition 4. Given , where  and  it follows that:
- If  and , then -  and ; 
- If  and , then - ; 
- If  and , then -  and ; 
- If  and , then - ; 
- If  and , then -  and ; 
- If  and , then -   and ; 
- If  and , then -  and ; 
- If  and , then -  and ; 
- If  and , then - ; 
- If  and , then -  and  
 Proof.  As the inclusion of two modal intervals 
 conforms to the following (Gardeñes, [
9]):
          
By applying Lemma 1 to the -cuts  and , which are modal intervals, we obtain the desired result. ☐
 Notice that the following six cases:
, , ,
, , , are not treated in the above Proposition 4, as they are dual cases of some of those studied, and it is possible to apply the property 
From the above Lemma 1, it is possible to express the inclusion relation of two MITFNs in terms of their coordinates. Thus, if 
, 
 and 
, then:
Definition 3. (Infimum and supremum)
Given ,
-  if  and if there exists a  such that and , then ; 
-  if  and if there exists a  such that and , then  
 Proposition 5. Given , let us consider  and , then  Proof.  We should consider the following four cases, depending on the modalities of  and 
-  and  are proper intervals. - As  and  are proper intervals, then A and B are MITFNs. Thus,  and  so  and as  and  are proper intervals, . If , then ,  and . Moreover, if  conforms to and , then  , and  so  and . That is, ; thus,  Inf 
-  is a proper interval and  an improper interval. - As  is a proper interval,  is an improper interval and   and it follows that  We distinguish the following two cases according to the inclusion set of  and :
               - (a)
- If , we can proceed as in the first case.   
- (b)
- If , then let us prove that  corresponds to . Notice that, if , then ; thus, . It is obvious that . Moreover,  and  as . In a similar way,  and . Therefore,  and . - If  conforms to and , then  Notice that, as  is an improper interval,  will also be an improper interval and then . - We must prove that  and, consequently, . - If  is an improper interval, as , it follows that  As , then .   
- If  is a proper interval, as  and , it follows that  Using the inclusion , we obtain  which contradicts the hypothesis . 
 
 
-  is an improper interval and  a proper one. - As  is an improper interval,  is a proper interval and  , and it follows that ; thus, the demonstration follows as in the first case. 
-  and  are improper intervals.
               - (a)
- If , then . Let us prove that  corresponds to . Notice that if , then  and thus . It is obvious that . Moreover,  and  as . In a similar way,  and . Therefore,  and . - If  conforms to and , then  and . This implies that  and  are improper intervals. - Moreover, as , then due to the modality of  and  it will be the case that  and as , it follows that . Thus,  
- (b)
- If , then . Let us prove that  corresponds to . If , then ,  and . Moreover, if  conforms to and , then  , and  so  and . That is, ; thus,  Inf 
- (c)
- If  or , then let us prove that  corresponds to . Notice that  as  and  are improper intervals,  thus  and obviously  and . - If  conforms to and , then  and . As  and  are both improper,  and  will also be improper intervals. - Moreover, as , then due to the modality of  and , it will be the case that  and as , it follows that  and consequently . That is , thus . 
 
☐
 Proposition 6. Given , let us consider  and , then:  Proof.  Applying modal interval properties relating duality and meet–join operators [
24], that is:
          
          and
          
Let 
 be 
. As 
, then
          
In the same way, if , then 
When we calculate Inf we should consider:
          
- If  - , then  -  and so:
               
- If  - , then
               
☐
   4. Interpretability of the Calculations
Definition 4. (Modal extension of a real function)
Let  be a real continuous function. We represent the modal interval trapezoidal fuzzy extension associated with f by  and we define it over  using the interval extension  (see Sainz [
24,
31]
) over the cuts of the MITFNs   As the 
-cuts are modal intervals, we can express this modal interval trapezoidal fuzzy extension using the meet and join operators, and splitting the components of 
 into the proper ones: 
 and the improper ones: 
      which is equivalent to
      
Proposition 7. (Inclusivity of the modal extension)
If  is the modal extension of a real continuous function , given  such that , then:  Proof.  We can express the inclusion of MITFNs by using the inclusion of the 
-cuts, that is 
, as the interval extension 
 is inclusive ([
24] [Theorem 3.2.4]).
        
☐
 Given a rational real continuous function 
f, if 
 are the 
-cuts of the classical trapezoidal fuzzy numbers 
, then 
, and the interval extension that we represent by 
 associated with 
f can be interpreted as:
      or also as:
      because 
f is a continuous function and the value 
 corresponds to:
In most of the cases, 
 the exact values 
 that define the fuzzy number 
 are difficult to calculate. This is the reason why we often replace every rational operator in the function 
f by its corresponding intervalar operator. The result obtained with this replacement is not the same interval 
, but an interval 
 which conforms to:
However, the only valid semantic application to this last calculus will be the interpretation given in Equation (
1)
      
      and the semantic expressed in Equation (
2) will not be applicable to this last calculus 
.
Theorem 1. (Interpretability theorem)
Let be the ∗-extension of a real continuous function , and let  be a vector of MITFNs sorted by their modality, that is:where  If  conforms to  and we consider   and  the transition modality values of the MITFNs  and Z, respectively, then, given  it holds that:
 such that:where  are the α-cuts of  whose interval modality is proper,  are the α-cuts of  whose interval modality is improper,  are the α-cuts of  whose interval modality is proper and  are the α-cuts of  whose interval modality is improper, and  if  or  if . Moreover,although the elements within these sets are not ordered in the same way.  Proof.  The interval semantic theorem (Sainz [
24]), states that if 
 is the ∗-semantic extension of a real function 
f, and 
X is a vector of modal intervals expressed as 
 where 
 are the proper components of 
X and 
 are the improper components of 
X, if 
 is such that 
, then:
        
We will now apply this semantic theorem to the 
-cuts of:
        
The modality of the -cuts  is always proper and the modality of the -cuts  is always improper.
Let 
 be the set 
. Above this set 
 we define inductively
        
Then, given , there will exist  such that  For this value, we must consider the modality of the -cuts  and , as some of these modalities have changed with regard to the modalities of the zero-cuts  .
Thus, for this given , there will be  in which the interval modality of their -cuts is proper, and there will be  in which the interval modality of their -cuts is improper. At the same time, there will exist  in which the interval modality of their -cuts is proper and  in which the interval modality of their -cuts is improper. The interval modality of  will be  if  and  if . ☐
 Corollary 1. Under the above conditions of Theorem 1, if , then:
 such that  Corollary 2. Under the above conditions of Theorem 1, if , then:
 such that  Definition 5. Let ⊙ 
be a binary real rational operator. Given : the extension of the operator ⊙ 
above the MITFNs A and B is represented by ⊗ 
and defined using the α-cuts of A and B as  Definition 6. Let ⊙ be a binary real rational operator, and ⊗ its extension above the MITFNs. Given  if C is an MITFN, C is said to be interpretability compatible with the exact value  if .
 Often, the result of calculating  will not be an MITFN. There are some situations that clearly reflect this, such as the multiplication, the quotient and rounding results. This situation is well known when working with trapezoidal fuzzy numbers, in which not all the rational operators are internal operators. To preserve the inclusion expressed in the above Theorem 1, that is, , we will have to find  such that .
The extension of a rational real operator ⊙ above two MITFNs A and B is always interpretability compatible with the calculation of  as the intervalar extension above the -cuts  and  is always inclusive.
Sometimes, rounding results do not constitute a very important subject. However, if we center our study on the interpretability of the calculus, then when we evaluate  we must find a modal trapezoidal fuzzy number C such that C is interpretability compatible with .
We have just mentioned Equation (
3) above, the difficulty of calculating the exact value
      
      of a rational real function 
f. The modal extension 
 of the real continuous function 
f is even more difficult to evaluate. Thus, instead of calculating the modal extension 
, we will evaluate a new function obtained by replacing every rational real operator in 
f by its corresponding operator above MITFNs. Since, on many occasions, the result of calculating these rational operators will not be an MITFN, this result will be transformed into an MITFN that is interpretability compatible with the exact result.
What we have laid out leads us to evaluate an MITFN Z that contains the exact value. Of course, this is too general and we should impose some other conditions.
Many methods to convert a non-trapezoidal fuzzy number to a trapezoidal one have been studied. Many researches have studied how to find a fuzzy number that is the nearest to a non-trapezoidal fuzzy number, which is related to the approximation of fuzzy numbers under different points of view.
Abbasbandy and Asady [
32] used the metric distance between two fuzzy numbers to introduce a trapezoidal approximation. Other research such as Grzegorzewski and Mrówka [
33], and Grzegorzewski [
34] and Yeh [
35,
36] studied a nearest trapezoidal approximation preserving the expected interval.
Veerani et al. [
37] proposed a method to convert any fuzzy number to the nearest symmetric trapezoidal fuzzy number approximation also preserving the expected interval.
Preserving ambiguity, value and width, Ban, Coroianu and Khastan [
38] developed a general method to study the L-R approximations of fuzzy numbers. In addition, some methods for ranking fuzzy numbers using distances have been developed [
39,
40], but none of those trapezoidal approximations is useful to us because, although they preserve certain properties, they do not impose preservation of inclusivity and so they are not valid for semantic interpretations.
When possible, we will apply the optimal external inclusion introduced by Wagen [
41], although, in some special cases, it may be necessary to add some further conditions, referring to the inclusion of the core of the result.
Example 1. The fuzzy trapezoidal equation  whose solution is  does not always have a solution in the classical sense. However, using MITFNs, many of these equations not only have a solution, but the solution can be semantically interpreted as well.
 Let us take the MITFN 
 and 
. Both 
A and 
B are proper trapezoidal fuzzy numbers, that is, trapezoidal fuzzy numbers in the classical sense. The solution of the equation
      
      is
      
However,  is an MITFN whose transition modality value is 
Thus, the interpretation is:
Next, let us consider the fuzzy equation 
, where 
A is the MITFN
, 
 and 
B is the MITFN
, 
 The solution for 
X is:
      which is an MITFN
 (see 
Figure 6). The transition modality value for 
X is 
 and the transition modality value for 
B is 
; thus, the interpretation of the calculus 
 is:
  5. Conclusions
In this paper, we have used the lattice structure of modal intervals to develop the lattice completion of trapezoidal fuzzy numbers, with regard to the inclusion relation. We have named the set obtained with this completion MITFNs. The elements of this new set have been defined allowing that their -cuts can be modal intervals and also allowing that the support modality and the core modality are not the same. This reticular completion has not simply been left in a theoretical study of the inclusion relationship between modal trapezoidal fuzzy numbers, but the calculation of the extensions of real continuous functions has also been addressed.
Moreover, we have not simply focused on the calculation of the real extensions on MITFNs, but we have also used the semantic theorem of modal interval analysis so as to interpret the calculus to the -cuts of the extensions. We are certain that knowing the meaning of a calculation is even more important than the calculation itself.
With the study presented in this paper, we have provided a new tool for fuzzy numbers. We have introduced an extension of traditional trapezoidal fuzzy numbers and we have solved a problem that had no solution in the set of traditional trapezoidal fuzzy numbers, while also providing the semantic interpretation of the result obtained.
Our future lines of research are twofold; on the one hand, further theoretical research will be conducted, and, on the other, some practical applications of our theoretical studies will be developed.
Regarding theoretical studies, we believe it is interesting to look for and implement algorithms that allow us to obtain a good inclusive approach to the semantic extension . Thus, we would reduce the typical enlargement of the interval results. This research should be supplemented with the study of optimality in the calculus of rational functions, understanding that optimality, in the sense of studying when a result obtained by replacing each of the rational operators in the real function f by the corresponding fuzzy operator, is the best possible result with regard to the inclusion relationship.
From an applied perspective, the application of MITFNs to the field of MultiCriteria Decision Making (MCDM) is also worth exploring, as there are many methods related to multicriteria analysis that use trapezoidal fuzzy numbers that could be extended to the MITFNs. Among these methods, we will pay special attention to the following: the CODAS method (Combinative Distance-based Assessment) [
42], QUALIFLEX method (QUALItative FLEXible) [
43], ELECTRE method (ELimination Et Choix Traduisant la REalité) [
44,
45], VIKOR method (VlseKriterijumska Optimizacija I Kompromisno Resenje) [
46,
47], MULTIMOORA method (Multiple Objective Optimization on the basis of Ratio Analysis) [
48], EAMRIT Method (Evaluation by an Area-based Method of Ranking Interval Type-2 Fuzzy sets) [
49], TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution [
50], EDAS Method (Evaluation based on Distance from Average Solution) [
51], AFRAW Method (Assessment-based on Fuzzy Ranking and Aggregated Weights) [
52], TEDE Method (Total Effective Dose Equivalent) [
53], and WASPAS (Weighted Aggregated Sum Product ASsessment) [
54]. The extension of these methods to the field of MITFNs can provide new tools, from the point of view of both the calculations, as well as from the interpretative point of view.