Abstract
In this paper, we introduce and study the GQL-operations. We prove that this class is a hyper class of the known class of QL-operations. Similar to QL-operations, GQL-operations are not always fuzzy implications. On the other hand, we present and prove a necessary but not sufficient condition that leads to the generation of a GQL-implication. Our study is completed by studying the satisfaction or the violation of some basic properties of fuzzy implications, such as the left neutrality property, the exchange principle, the identity principle and the left ordering property. Our study also completes the study of the aforementioned basic properties for QL-implications and leads to a new connection between QL-operations and D-operations.
MSC:
03B52; 68T27; 94D05
1. Introduction
Many definitions and properties in fuzzy theory have been generated from generalizations of classical tautologies [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. Moreover, fuzzy implications have been used for the construction of many applications [14,24,25] and are applied in many different scientific and applicable areas [26,27,28,29,30,31,32,33] such as approximate reasoning, control theories and expert systems, robotics, decision-making theories, fuzzy mathematical morphology, image processing, and others. The necessity of construction methods, or different classes, or different models of fuzzy implications is remarked by Mas et al. [14]. According to Mas et al. [14], new fuzzy implication functions are necessary, since they can be adequate in specific applications.
In [4], a construction method of new classes of fuzzy implications via known classes of fuzzy implications was presented, and their connection has also been studied (between the preliminary and the induced class). As a result, the third application of this method in [4] leads to the class of QL-implications, which is the generalization of the following classical tautology
In this paper, we revisit QL-implications, and we generalize them. This generalization was achieved via the possible usage of not only the same fuzzy negation function everywhere in their formula. Their formula contains three times a fuzzy negation function in it, and there is no restriction in the usage of the same fuzzy negation everywhere in the construction formula in every generation. Therefore, we are leading to a new generalized class of QL-implications (shortly GQL-implications). This paper follows very recent studies that have been published [5,11,34]. In other words, this is a following study, which generalizes the class of QL-implications [4] and generates a variety of fuzzy implications, which is necessary according to many authors [9,13,14].
The paper is organized as follows. Section 2 provides the preliminaries concepts for understanding the article. Section 3 contains the main results and is divided into five subsections. In Section 3.1, we introduce GQL-operations. We prove that they are a hyper class of QL-operations but not always fuzzy implications. A necessary but not sufficient condition for a GQL-operation to be a fuzzy implication is proved. A connection of GQL-, QL-, D- and D-operations is also presented. In Section 3.2, we present a sufficient and necessary condition for the satisfaction of the left neutrality property by a GQL-operation. Some more results for the violation of the left neutrality property by a GQL-operation are also presented. In Section 3.3, there is a similar study for the exchange principle property and GQL-operations. A sufficient condition for the violation of the exchange principle by a GQL-operation is presented. Another result for the violation of the exchange principle by a GQL-operation is also presented. In Section 3.4, we deal with the identity principle property and GQL-operations. Some equivalent statements under specific conditions are proved. In Section 3.5, it is proved that the identity principle and the left ordering property are equivalent for GQL-operations. Some equivalent statements under specific conditions are also proved. The results of Section 3.3, Section 3.4, Section 3.5 also complete the study of the basic properties of QL-implications and conditions, assuming the three fuzzy negations in the construction method as equal, i.e., as one. Section 4 contains the results of the study. A new connection of QL- and D-operations comes out of this study. Nevertheless, this result is not the aim of this study, and it encourages the necessity of such studies. Section 5 contains the potential advantages of GQL-implications. Section 6 contains the conclusions.
2. Preliminaries
Definition 1
([1,35,36,37]). A decreasing function is called fuzzy negation if and . Moreover, a fuzzy negation N is called
- (i)
- Strict if it is continuous and strictly decreasing;
- (ii)
- Strong if it is an involution, i.e.,
- (iii)
- Non-filling, if
Example 1.
- (i)
- The so-called, least and greatest fuzzy negations (see [1] Example 1.4.4) are, respectively,
- (ii)
- We call the classical fuzzy negation, which is a strong negation. Moreover, there are several types of fuzzy negations, such as and (see [1] Example 1.4.4 and Table 1.6).
Lemma 1
([1] Lemma 1.4.9). If , are fuzzy negations such that , then
- (i)
- is a continuous fuzzy negation;
- (ii)
- is a strictly decreasing fuzzy negation.
Definition 2
([1,36,37]). A function is called a triangular norm (shortly t-norm) if it satisfies, for all , the following conditions:
Example 2.
- (i)
- There are several types of t-norms, such as the minimum t-norm , the Łukasiewicz t-norm and the nilpotent minimum t-norm(see [1] Table 2.1).
- (ii)
- Moreover, there are several types of t-conorms, such as the probabilistic t-conorm (see [1] Table 2.2).
Definition 3
([1] Definition 2.1.2). A t-norm T is called
- (i)
- Idempotent, if
- (ii)
- Positive, if
Definition 4
([1,37]). A t-norm T is strictly monotone if whenever and .
Proposition 1
([3] Proposition 9). For all it is
Remark 1.
By Proposition 1, it follows that
and
Definition 5
([1] Definition 2.3.14). Let T be a t-norm and N be a fuzzy negation. We say that the pair satisfies the law of contradiction if
Definition 6
([37] p. 232). Let N be a strict negation, S be a t-conorm and T be a t-norm, such that . Then, S is said to be the N-dual of T, and we denote it by .
Respectively, we have the following Definition.
Definition 7.
Let N be a strict negation, T be a t-norm and S be a t-conorm, such that . Then, T is said to be the N-dual of S, and we denote it by .
Definition 8
([1] Definition 2.3.1). Let T be a t-norm. A function defined as
is called the natural negation of T or the negation induced by T.
Remark 2.
- (i)
- It is easy to prove that is a fuzzy negation (see [1] Remark 2.3.2. (i)).
- (ii)
- If for some , then (see [1] Remark 2.3.2. (iii)).
Corollary 1
([1] Corollary 2.3.7). Let T be any t-norm and be its natural negation. Then, the following statements are equivalent:
- (i)
- is strictly decreasing.
- (ii)
- is continuous.
- (iii)
- is strong.
Proposition 2
([1] Proposition 2.3.4). If a t-norm T is left continuous, then for every x, y , it is
Definition 9
([1,38]). By Φ, we denote the family of all increasing bijections from to . We say that functions are Φ-conjugate if there exists a such that , where
Remark 3
([1] Propositions 1.4.8, Remarks 2.1.4 (vii) and 2.2.5 (vii)). It is easy to prove that if and T is a t-norm, S is a t-conorm and N is a fuzzy negation (respectively, strict and strong), then is a t-norm, is a t-conorm and is a fuzzy negation (respectively, strict and strong).
Definition 10
([1,35]). A function is called a fuzzy implication if
Remark 4.
Moreover, by Definition 10, it is easy to prove the left and right boundary conditions [1]
Definition 11
([1,10]). A fuzzy implication I is said to satisfy
- (i)
- The left neutrality property, if
- (ii)
- The exchange principle, if
- (iii)
- The identity principle, if
- (iv)
- The ordering property, if
- (v)
- The left ordering property, if
- (vi)
- The right ordering property, if
Remark 5.
- (i)
- (ii)
Lemma 2
Definition 12
([1] Definition 1.4.15). Let be a fuzzy implication. The function defined by Lemma 2 is called the natural negation of I.
Definition 13
([1] Definition 1.6.12). Let N be a fuzzy negation and I be a fuzzy implication. A function defined by
is called the N-reciprocal of I.
Theorem 1
([1] Theorem 1.6.2). If N is a fuzzy negation and I is a fuzzy implication, then is a fuzzy implication.
Remark 6.
The definition of the N-reciprocal of a function is not limited to fuzzy implications, but in any function .
Definition 14
([1,13,14,35,39]). A function is called a QL-operation if there exist a t-norm T, a t-conorm S and a fuzzy negation N such that
If I is a QL-operation generated from the triple , then we will often denote it by .
Definition 15
([1,13,39,40]). A function is called a D-operation if there exist a t-norm T, a t-conorm S and a fuzzy negation N such that
If I is a D-operation generated from the triple , then we will often denote it by .
Definition 16
([4] Definition 12). A function is called a D-operation if there exist a t-conorm S, a t-norm T and a fuzzy negation N such that
If I is a D-operation generated by the triple , then we denote it by .
Definition 17
([4] Definition 13). A function is called a QL-operation if there exist a t-norm T, a t-conorm S and a fuzzy negation N such that
If I is a QL-operation generated by the triple , then we denote it by .
Remark 7
([1,4,39,40]). QL-, QL- and D-, D-operations are not fuzzy implications in general, since (12) (for QL- and QL-operations) or (13) (for D- and D-operations) could not hold, respectively. Only if the QL- or QL- or D- or D-operation is a fuzzy implication, we will use the term QL- or QL or D- or D-implication, respectively.
3. The Main Results
3.1. GQL-Implications
Generalized QL-implications are constructed by using not only the same fuzzy negation function to their formula. So, we are leading to the following definition.
Definition 18.
A function is called a GQL-operation if there exist a t-norm T, a t-conorm S and three fuzzy negations , and such that
If I is a GQL-operation generated by the quintuple , then we denote it by .
Proof.
Let be a GQL-operation, then for ,
which means that satisfies (13).
satisfies (14), since
satisfies (15), since
satisfies (16), since
satisfies (17), since
satisfies (18), since
Lastly, we have
for all . □
Remark 8.
Note that if , the corresponding GQL-operation is a QL-operation. This means that GQL-operations do not always satisfy (12). Moreover, Example 3 leads us to the same result even if we use different negations. Therefore, we called them GQL-operations instead of GQL-implications.
Example 3.
Consider the quintuple . The corresponding GQL-operation is
which is not a fuzzy implication, since
Thus, violates (12).
Proposition 3.
A function is called a GQL-implication if it is a GQL-operation and satisfies (12).
Proof.
The proof is obvious. □
Example 4.
Consider the quintuple . The corresponding GQL-operation, which is a GQL-implication, is
([2] Figure 5).
Remark 9.
According to the Example 4, there are GQL-implications that are not QL-implications, since the only QL-implication that has as its natural negation is
([4] Remark 5.14).
Since the natural negation of the GQL-implication is also , we deduce that is not a QL-implication. Thus, the class of GQL-implications is a hyper class of that of QL-implications. According to Example 4, it is a non-empty set. According to Remark 8, it is a new class of fuzzy implications that contains QL-implications.
Proposition 4.
Let be a GQL-implication and be a non-filling fuzzy negation. Then, the pair satisfies (11).
Proof.
If is a fuzzy implication, then it satisfies (19). Therefore, for all , it is
Therefore, the pair satisfies (11). □
Example 5.
Consider the quintuple . The corresponding GQL-operation is
which is not a fuzzy implication, since
Thus, violates (12).
Remark 10.
- (i)
- Proposition 4 gives a necessary but not sufficient condition for a GQL-operation to be a fuzzy implication, when is a non-filling fuzzy negation. Note that is a non-filling fuzzy negation, satisfies (11), but is not a fuzzy implication (see Example 5).
- (ii)
- By Proposition 4, it is obvious that if is a non-filling negation and the pair does not satisfy the law of contradiction (11), i.e., , for some , then the obtained GQL-operation, for any t-conorm S and fuzzy negation , is not a fuzzy implication.
Theorem 3.
By any quintuple , where is any non-filling fuzzy negation, S is any t-conorm, is any continuous fuzzy negation, is any fuzzy negation and T is any idempotent, strict or positive t-norm, it cannot be obtained any GQL-implication.
Proof.
The proof is similar to the proof of Theorem 5 in [4], by using Remark 10 (ii). Therefore, it is omitted. □
Theorem 4.
If and is a GQL-operation (respectively, implication), then is a GQL-operation (respectively, implication) and moreover,
Proof.
Let be a GQL-operation (respectively, implication), then is a GQL-operation (respectively, implication) according to Remark 5 (ii). Moreover, for all , we deduce that
□
A connection between GQL-, QL-, D- and D-operations is the following.
Theorem 5.
Let be a GQL-operation generated from a t-norm T, a t-conorm S, a strict fuzzy negation N and its inverse . Then,
Moreover, if one of , , and is a fuzzy implication, then the other three are fuzzy implications, too.
3.2. GQL-Implications and the Left Neutrality Property (20)
Proposition 5.
Let be a GQL-operation. Then, satisfies (20) if and only if
Proof.
Let be a GQL-operation. If , then for all , it is
Thus, satisfies (20).
Conversely, if satisfies (20), then for all
□
Corollary 2.
Let be a GQL-operation, where at least one of and is a two-valued fuzzy negation, i.e., or . Then, violates (20).
Proof.
If at least one of and is a two-valued fuzzy negation, then . So, if we assume that satisfies (20), that is a contradiction according to Proposition 5. □
According to Lemma 1 and Proposition 5, we obtain the following corollaries. Their proofs are omitted, since they are obvious.
Corollary 3.
Let be a GQL-operation, where is not a continuous fuzzy negation. Then, violates (20).
Corollary 4.
Let be a GQL-operation, where is not a strictly decreasing fuzzy negation. Then, violates (20).
3.3. GQL-Implications and the Exchange Principle (21)
Proposition 6.
Let be a GQL-operation. If satisfies (21), then .
Proof.
We assume that satisfies (21). Then, for all
□
Example 6.
Let be the Schweizer–Sklar t-conorm ([1] Example 2.6.15), then for ,
.
Remark 11.
- (i)
- Proposition 6 gives us the sufficient condition that if there is an such that , then violates (21).
- (ii)
Proposition 7.
Let be a GQL-operation, where is strictly decreasing with a fixed point. If does not have any fixed point, or and have different fixed points, then violates (21).
Proof.
The proof is similar with the proofs of Proposition 3.13 in [5] and Proposition 7 in [34]. Therefore, it is omitted. □
3.4. GQL-Implications and the Identity Principle (22)
Proposition 8.
Let the quintuple be , where is any non-filling fuzzy negation. If is strong and the corresponding GQL-operation, satisfies (22), then
Proof.
□
Theorem 6.
Let the quintuple be given, where is any non-filling fuzzy negation. Let be a strong fuzzy negation and T be a left continuous t-norm. Then, the following statements are equivalent:
- (i)
- satisfies (22).
- (ii)
- , for any .
Proof.
(i) ⇒ (ii) The proof is obvious. Just apply Proposition 8.
(ii) ⇒ (i) For any x
□
Remark 12.
Note that Proposition 8 and Theorem 6 hold when is strictly decreasing, or continuous, since it is also strong, according to Corollary 1.
3.5. GQL-Implications and the Left Ordering Property (24)
It is obvious that the satisfaction of the ordering property (23) implies the satisfaction of the identity principle (22). Moreover, the ordering property (23) is divided in two sub-properties, the so-called left ordering property (24) and right ordering property (25). It is easy to observe that if a fuzzy implication I satisfies both (24) and (25), it satisfies the ordering property (23). In the following, we prove a useful theorem.
Theorem 7.
Proof.
Thus, I satisfies the left ordering property (24).
Thus, I satisfies the identity principle (22). □
Corollary 5.
Proof.
By virtue of Theorem 2, satisfies (13). Therefore, the proof is deduced by applying Theorem 7. □
Corollary 6.
Let the quintuple be given, where is any non-filling fuzzy negation. Let be a strong fuzzy negation and T be a left continuous t-norm. Then, the following statements are equivalent:
Proof.
By virtue of Theorem 6 and Corollary 5, the proof is obvious. □
4. Results
In this paper, we have introduced a hyper class of the known class of QL-implications. This hyper class has been named GQL-implications. Similar to QL-implications, they are not always fuzzy implications, since they sometimes violate (12). Therefore, we use the term GQL-operations instead of implications. The sufficient and necessary conditions under a GQL-operation that is a fuzzy implication have not been discovered, and the same open problem holds for QL-, QL-, D- and D-operations, respectively.
Moreover, it has been proved that the class of GQL-operations is a hyper class of that of QL-operations. A necessary but not sufficient condition for a GQL-operation to be a fuzzy implication has been proved (see Proposition 4 and Remark 10). A relation between GQL-, QL-, D- and D-operations has been presented and proved (see Theorem 5). In Theorem 3, quintuples that do not generate GQL-implications have been excluded. In Theorem 4, the relation of -conjugation in GQL-operations was studied.
In the following subsections, the basic properties (20)–(22) have been studied extensively. The sufficient and necessary condition under a GQL-operation satisfies (20), which has been presented and proved (see Proposition 5). Moreover, a sufficient condition under a GQL-implication does not satisfy (21), which has been presented and proved. A study for the satisfaction of (22) has also been made, and some more general results for the satisfaction of (22) and (24) have been presented. The equivalence of (22) and (24) in GQL-operations has also been proved.
Lastly, we have to remark a result that was not the aim of this study, but it came out. In [4], Theorem 4 has been proved that a D-operation is the reciprocal of a QL-operation when they are generated from a strong fuzzy negation. In Theorem 5, Equation (26) expands this result by substituting strong fuzzy negations with strict fuzzy negations. Thus, a D-operation is the reciprocal of a QL-operation when they are generated from a strict fuzzy negation, as it is described in Equation (26).
5. Discussion
There is a great repertoire of fuzzy implication functions that is also uncountable. Do we need more? Fuzzy logic is mostly generated from classical logic, but it is not classical logic. Although in classical logic everything is unique and uniquely determined, this uniqueness fortunately does not hold in fuzzy logic. Moreover, we have not stopped with the definition of a fuzzy implication via the five axioms (12)–(16); we expand to many other properties that restrict the number of fuzzy implications in order to be useful to specific applications. Therefore, it is ensured that we need more fuzzy implications.
What is the benefit of this study? We believe that the main benefit of this hyper class is the variety of the generated fuzzy implications. We obtain this variety from the possible usage of up to three different fuzzy negations in the generation formula. Moreover, we can generate many fuzzy implications which have the same fuzzy negation by stabilizing in their formula.
6. Conclusions
In this paper, a hyper class of fuzzy implications, the so-called GQL-implications, was introduced and studied. The main idea was to use not only the same fuzzy negation in their formula, which is a fact that increases the variety of fuzzy implications we can obtain. Unfortunately, they are not always fuzzy implications. It remains an open problem whether a GQL-operation is a fuzzy implication. Moreover, some properties of fuzzy implications, such as (20)–(22) and (24) for GQL-operations, have been studied. Surprisingly, a new connection of QL- and D-operations has also been discovered. This connection was discovered accidentally and is another reason for the necessity of this study. Lastly, this study completes the study of some basic properties of QL-operations.
Author Contributions
Supervision, B.P.; writing—original draft, D.S.G. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
No data were used to support this study.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Baczyński, M.; Jayaram, B. Fuzzy Implications; Springer: Berlin/Heidelberg, Germany, 2008. [Google Scholar]
- Drewniak, J. Invariant fuzzy implications. Soft Comput. 2006, 10, 506–513. [Google Scholar] [CrossRef]
- Grammatikopoulos, D.C.; Papadopoulos, B.K. A Method of Generating Fuzzy Implications with Specific Properties. Symmetry 2020, 12, 155. [Google Scholar] [CrossRef]
- Grammatikopoulos, D.C.; Papadopoulos, B.K. An Application of Classical Logic’s Laws in Formulas of Fuzzy Implications. J. Math. 2020, 2020, 8282304. [Google Scholar] [CrossRef]
- Grammatikopoulos, D.C.; Papadopoulos, B.K. A study of (T,N)- and (N′,T,N)-Implications. Fuzzy Inf. Eng. 2021, 13, 277–295. [Google Scholar] [CrossRef]
- Dimuro, G.P.; Bedregal, B.; Bustince, H.; Jurio, A.; Baczyński, M.; Mis, K. QL-operations and QL-implication functions constructed from triples (O,G,N) and the generation of fuzzy subsethood and entropy measures. Int. J. Approx. Reason. 2017, 82, 170–192. [Google Scholar] [CrossRef]
- Fodor, J. On fuzzy implication operators. Fuzzy Sets Syst. 1991, 42, 293–300. [Google Scholar] [CrossRef]
- Fodor, J. A new look at fuzzy connectives. Fuzzy Sets Syst. 1993, 57, 141–148. [Google Scholar] [CrossRef]
- Pinheiro, J.; Bedregal, B.; Santiago, R.H.N.; Santos, H. (T,N)- implications. In Proceedings of the 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy, 9–12 July 2017; pp. 1–6. [Google Scholar] [CrossRef]
- Pinheiro, J.; Bedregal, B.; Santiago, R.H.N.; Santos, H. A study of (T,N)-implications and its use to construct a new class of fuzzy subsethood measure. Int. J. Approx. Reason. 2018, 97, 1–16. [Google Scholar] [CrossRef]
- Pinheiro, J.; Bedregal, B.; Santiago, R.H.N.; Santos, H. (N′, T, N)-Implications. In Proceedings of the 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Rio de Janeiro, Brazil, 8–13 July 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Pinheiro, J.; Bedregal, B.; Santiago, R.H.N.; Santos, H.; Dimuro, G.P. (T, N)-Implications and Some Functional Equations. In Proceedings of the Fuzzy Information Processing, NAFIPS 2018, Communications in Computer and Information Science, Fortaleza, Brazil, 4–6 July 2018; Barreto, G., Coelho, R., Eds.; Springer: Cham, Switzerland, 2018; Volume 831, pp. 302–313. [Google Scholar] [CrossRef]
- Trillas, E.; Mas, M.; Monserrat, M.; Torrens, J. On the representation of fuzzy rules. Int. J. Approx. Reason. 2008, 48, 583–597. [Google Scholar] [CrossRef][Green Version]
- Mas, M.; Monserrat, M.; Torrens, J.; Trillas, E. A Survey on Fuzzy Implication Functions. IEEE Trans. Fuzzy Syst. 2007, 15, 1107–1121. [Google Scholar] [CrossRef]
- Bandler, W.; Kohout, L.J. Semantics of implication operators and fuzzy relational products. Int. J. Man Mach. Stud. 1980, 12, 89–116. [Google Scholar] [CrossRef]
- Dubois, D.; Prade, H. A theorem on implication functions defined from triangular norms. BUSEFAL 1984, 18, 33–41. [Google Scholar]
- Fodor, J.C. A remark on constructing t-norms. Fuzzy Sets Syst. 1991, 41, 195–199. [Google Scholar] [CrossRef]
- Fodor, J.C. Strict preference relations based on weak t-norms. Fuzzy Sets Syst. 1991, 43, 327–336. [Google Scholar] [CrossRef]
- Miyakoshi, M.; Shimbo, M. Solutions of composite fuzzy relational equations with triangular norms. Fuzzy Sets Syst. 1985, 16, 53–63. [Google Scholar] [CrossRef]
- Trillas, E.; Valverde, L. On implication and indistinguishability in the setting of fuzzy logic. In Management Decision Support Systems Using Fuzzy Sets and Possibility Theory; Kacprzyk, J., Yager, R.R., Eds.; Verlag TI: Rheinland, Germany, 1985; pp. 198–212. [Google Scholar] [CrossRef]
- Weber, S. A general concept of fuzzy connectives, negations and implications based on t-norms and t-conorms. Fuzzy Sets Syst. 1983, 11, 115–134. [Google Scholar] [CrossRef]
- Wilmott, R. Two fuzzier implication operators in the theory of fuzzy power sets. Fuzzy Sets Syst. 1980, 4, 31–36. [Google Scholar] [CrossRef]
- Yager, R. An approach to inference in approximate reasoning. Int. J. Man Mach. Stud. 1980, 13, 323–338. [Google Scholar] [CrossRef]
- Baczyński, M. On the applications of fuzzy implication functions. In Soft Computing Applications; Balas, V.E., Fodor, J., Várkonyiczy, A.R., Dombi, J., Jain, L.C., Eds.; AISC Springer: Berlin/Heidelberg, Germany, 2013; Volume 195, pp. 9–10. [Google Scholar] [CrossRef]
- Baczyński, M.; Beliakov, G.; Bustince, H.; Pradera, A. Advances in Fuzzy Implication Functions. In Studies in Fuzziness and Soft Computing; Springer: Berlin/Heidelberg, Germany, 2013; Volume 300. [Google Scholar] [CrossRef]
- Bloch, I. Duality vs. adjunction for fuzzy mathematical morphology and general form of fuzzy erosions and dilations. Fuzzy Sets Syst. 2009, 160, 1858–1867. [Google Scholar] [CrossRef]
- Bustince, H.; Fernández, J.; Sanz, J.; Baczyński, M.; Mesiar, R. Construction of strong equality index from implication operators. Fuzzy Sets Syst. 2013, 211, 15–33. [Google Scholar] [CrossRef]
- Cruz, A.; Bedregal, B.C.; Santiago, R.H.N. On the characterizations of fuzzy implications satisfying I(x, I(y, z)) = I(I(x, y), I(x, z)). Int. J. Approx. Reason. 2018, 93, 261–276. [Google Scholar] [CrossRef]
- Jayaram, B. On the law of importation ((x∧y)→z)≡(x→(y→z)) in fuzzy logic. IEEE Trans. Fuzzy Syst. 2008, 16, 130–144. [Google Scholar] [CrossRef]
- Štěpnička, M.; De Baets, B. Implication-based models of monotone fuzzy rule bases. Fuzzy Sets Syst. 2013, 232, 134–155. [Google Scholar] [CrossRef]
- Reiser, R.; Bedregal, B.; Baczyński, M. Aggregating fuzzy implications. Inf. Sci. 2013, 253, 126–146. [Google Scholar] [CrossRef]
- Yager, R.R. On some new classes of implication operators and their role in approximate reasoning. Inf. Sci. 2004, 167, 193–216. [Google Scholar] [CrossRef]
- Pradera, A.; Beliakov, G.; Bustince, H.; De Baets, B. A review of the relationships between implication, negation and aggregation functions from the point of view of material implication. Inf. Sci. 2016, 329, 357–380. [Google Scholar] [CrossRef]
- Grammatikopoulos, D.C.; Papadopoulos, B.K. A Study of GD′-implications, a New Hyper Class of Fuzzy Implications. Mathematics 2021, 9, 1925. [Google Scholar] [CrossRef]
- Fodor, J.C.; Roubens, M. Fuzzy Preference Modelling and Multicriteria Decision Support; Kluwer: Dordrecht, The Netherlands, 1994. [Google Scholar]
- Gottwald, S. A Treatise on Many-Valued Logics; Research Studies Press: Baldock, UK, 2001. [Google Scholar]
- Klement, E.P.; Mesiar, R.; Pap, E. Triangular Norms; Kluwer: Dordrecht, The Netherlands, 2000. [Google Scholar]
- Kuczma, M. Functional Equations in a Single Variable; PWN–Polish Scientific Publishers: Warsaw, Poland, 1968. [Google Scholar]
- Mas, M.; Monserrat, M.; Torrens, J. QL-implications versus D-implications. Kybernetika 2006, 42, 351–366. [Google Scholar]
- Massanet, S.; Torrens, J. Intersection of Yager’s implications with QL and D-implications. Int. J. Approx. Reason. 2012, 53, 467–479. [Google Scholar] [CrossRef]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).