1. Introduction
Fuzzy connectives play a crucial role in many applications of fuzzy logic, such as approximate reasoning, formal methods of proof, inference systems, and decision support systems. Recognizing the above importance, many methods of creating fuzzy connectives have been discovered. Most of them refer to the t-norms and I-implications fuzzy connectives. These methods, as well as the fuzzy connectives they produce, are visible in
Figure 1.
In 1942, Menger, in his paper “Statistical metrics”, was the first to use the concept of t-norms [
1]. Schweizer B. and Sklar A., in work published in 1958, 1960, 1961 and 1983 [
2], defined the axioms of ordinary rules and presented the results that occurred during development. Then, Ling C.H., in 1965 [
3], built upon B. Schweizer’s and A. Sklar’s work and defined the Archimedean t-norms. Frank M.J., in 1979 [
4], defined the parameterized families of t-norms. Finally, Navara M. in 1999 [
5], Gottwald S. in 2000 [
6] and Klement E.P. in 2001 [
7] introduced the method of producing t-norms via automorphism and additive generator functions.
Kerre E., Huang C. and Ruan D. discovered the modus ponens and modus tollens in 2004 [
8]; Trillas E., Mas M., Monserrat M. and Torrens J., in 2008, discovered different implications with varying properties [
9]. Thereafter, in 2004, Kerre E. and Nachtegael M. formed the fuzzy mathematical morphology [
10]. Furthermore, Bustince H. et al., in 2006, discovered fuzzy measures and image processing [
11]. Moreover, Baczyński M. and Jayaram B., as well as Mas M., Monserrat M., Torrens J. and Trillas E., in 2007, created the first strategy, which generates (S,N)-implications [
12,
13]; Fodor J.C. and Roubens M., in 1994, created the second strategy, which generates R-Implications [
14]. The third strategy, which generates QL and D-operations, was created by Mas M., Monserrat M. and Torrens J. in 2006 [
15]. In 2004, Yager R.R. created the fourth strategy, which generates f- and g-implications [
16]. Finally, Bustince H., Burillo P. and Soria F. in 2003 [
17], as well as Callejas C., Marcos J. and Bedregal B. in 2012, created the fifth strategy, which generates any fuzzy implication [
18].
Since 2012, there has been no further research focused on the fuzzy connectives. Therefore, this paper was created in order to build upon the previous discoveries and improve them by creating a faster and more flexible strategy for producing fuzzy connectives, which, in turn, produces more flexible results.
2. Literature Review
In the Introduction, a review of milestones achieved by other researchers in the field of fuzzy connectives was given. However, this section is dedicated to the presentation of published research of other researchers in the field of the generalization of fuzzy connectives. The goal of this presentation is the exploration of other viewpoints on the subject of this paper. In the following table, the research published for every primary category of fuzzy connectives is presented:
The field of the generalization of fuzzy connectives has been explored by many researchers over the years. As a result, the four main categories of fuzzy connectives have been the subject of many research papers which contributed to the development of the field.
The published research of the negation connectives category (see
Table 1) offered many contributions to the field of the generalization of fuzzy connectives. To be more specific, the book
Fuzzy Preference Modelling and Multicriteria Decision Support (see [
14]) and paper “Related Connectives for Fuzzy Logics” (see [
19]) contributed by offering definitions, properties and theorems. The paper “A treatise on many-valued logics” (see [
6]) contributed by offering a new strategy for generalizing fuzzy connectives via automorphisms.
Similarly, for the conjunction connectives: The paper “A Treatise on Many-Valued Logics” (see [
6]) contributed by offering new methods for generalizing conjunction connectives. The paper “Triangular norms” (see [
7]) contributed by offering new methods for constructing t-norms as well as t-norm families. The paper “Characterization of Measures Based on Strict Triangular Norms” (see [
5]) contributed by offering new strategies for producing t-norms and especially Frank’s t-norms. The paper “The best interval representations of t-norms and automorphisms” (see [
20]) contributed by offering new methods of producing t-norms, especially interval t-norms and interval automorphisms.
Similarly, for the disjunction connectives: The paper “Connectives in Fuzzy Logic” (see [
21]) contributed by offering new triples of t-norms, t-conorms and n-negations, which prove multiple theorems. The book
Fuzzy Implications (see [
22]) contributed by offering a complete presentation of the published research until 2008. The paper “A treatise on many-valued logics” (see [
6]) contributed by offering a combination of t-norms and t-conorms, which proves multiple theorems. The paper “Triangular norms” (see [
7]) contributed by offering a combination of t-norms and t-conorms, which proves multiple definitions and properties.
Finally, for the implication connectives: The book
Fuzzy Implications (see [
22]) contributed by offering a complete presentation of the published research until 2008. The paper “Automorphisms, negations and implication operators” (see [
17]) contributed by offering a new strategy for constructing implications via automorphisms. The paper “Actions of Automorphisms on Some Classes of Fuzzy Bi-implications” (see [
18]) contributed by offering a new class of implications, using automorphisms, the bi-implications class.
4. Materials and Methods
In this section, the methods used in this paper are presented in detail.
The following theorem presents the general form of fuzzy negations using automorphism functions. The researchers (J.C. Fodor and M. Roubens, Theorem 1.1, p. 4, [
14]), (Gottwald S., Theorem 5.2.1 p. 86, [
6]) and (Fodor J., p. 2077, [
19]) have worked with functions of this type, but they focused mainly on natural negations. The general formula (
1) can be used in order to generate new fuzzy negations (see Example 1i.).
Theorem 1. Let be a function. is a strong negation if and only if there is another strong negation N and an automorphism φ such that: Proof of Theorem 1. (⇒)
It is easy to see that the function
is defined by (
1) and is an involution with the properties
and
. In addition, it is strictly decreasing. Hence,
is a strong negation function (see Bedregal B.C., Proposition 3.2, p. 1127, [
23]).
(⇐)
We will prove that a strong negation
is written in the form (
1).
Let be a function be a strong negation and satisfy the following:
,
,
,
, and
.
Suppose there is a fixed point .
Additionally assume there is a strictly increasing, bijective function
Let a function N be a strong negation in [0, 1] with .
We define a function
with formula
We will prove that is an automorphism function.
Indeed:
If , then is a strictly increasing function.
If , then is a strictly decreasing function and h is a strictly increasing function. Then is a strictly decreasing function. Thus, is a strictly increasing function in [0, 1].
Therefore,
is a strictly increasing function in [0, 1].
Therefore, is an automorphism function.
We define the inverse function with the formula:
If
, then
If
, then
Consequently, Formula (
1) applies. □
The following theorem presents the general form of t-norms using an automorphism function. Researchers (see René B. et al., Theorem 2.3, p. 372, [
20]) and (Gottwald S., Theorem 5.1.3, p. 82, [
6]) worked with such functions, but they focused mainly on the specific forms of t-norms (see
Table 3). Formula (
2) can be used to generate new t-norms (see Example 1ii).
Theorem 2. Let be a function. is a strict and Archimedean t-norm if and only if there is another strict and Archimedean t-norm T and an automorphism φ such that: Proof of Theorem 2. (⇒)
We will prove that Formula (
2) is a strict and Archimedean t-norm.
Therefore, the function
is commutative.
Therefore, the function
is associative.
Therefore, the function
is monotonous with respect to the second variable.
Therefore, the function satisfies the boundary condition.
The function
is continuous with respect to the two variables.
Therefore, the function is Archimedean.
Consequently, the function given by Formula (
2) is a strict and Archimedean t-norm.
(⇐)
From the theorem of the additive generator, we obtain:
, where the function
f is a strictly decreasing function,
,
and
(see Baczyński M., Theorem 2.1.5, p. 43, [
22]) and (Gottwald S., Theorem 5.1.2, p. 78, [
6]).
We define the function
with the formula:
where
h is a strictly increasing function in [0, 1],
and
.
The function
h is inverted with the inverse:
Consequently, . □
Theorems 3–5 produce the same t-conorm. To be more specific, Theorem 3 presents the general form of t-conorms using an automorphism function. Formula (
3) can be used to generate new t-conorms (see Example 1iii).
Theorem 3. Let be a function which is a strict and Archimedean t-conorm if and only if there is another strict and Archimedean S t-conorm and an automorphism φ such that: Proof of Theorem 3. (⇒)
We will prove that Formula (
3) is a strict and Archimedean t-conorm.
Therefore, the function
is commutative.
Therefore, the function is associative.
, then it is monotonous.
Therefore, the function is monotonous.
The boundary condition applies to the function .
Consequently, the function is a t-conorm.
The function is continuous with respect to the two variables.
For a continuous t-conorm , the Archimedean property is given by the simpler condition .
Indeed,
holds because the function
S is Archimedean. Therefore, the function
is Archimedean. Consequently, the function
given by Formula (
3) is a strict and Archimedean t-conorm.
(⇐)
From the theorem of additive generators, we obtain:
, where the function is strictly increasing,
,
and
(see Baczyński M., Theorem 2.2.6, p. 47, [
22]).
We define the function with the formula , where h is a strictly increasing function in [0, 1], and .
The function
h is inverted with inverse:
Consequently, . □
The following theorem presents the general form of t-conorms using an automorphism function according to the equation
(see Klement E.P., Proposition 1.15, p. 11 [
7]), (Alsina C., Definition 3.3, p. 2, [
21]) and (see Baczyński M., Proposition 2.2.3, p. 46, [
22]). Formula (
4) can be used to generate new t-conorms (see Example 1iv).
Theorem 4. If there exists a continuous (Archimedean, strict, nilpotent) t-norm and an automorphism φ such that is defined bythen is a continuous (Archimedean, strict, nilpotent) t-conorm. Proof of Theorem 4. From (Klement E.P., Proposition 1.15, p. 11 [
7]), (Alsina C., Definition 3.3, p. 2, [
21]) and (Baczyński M., Proposition 2.2.3, p.46, [
22]),
Therefore, the function
satisfies the commutativity property.
Therefore, the function
satisfies the associativity property.
Therefore, the function
satisfies the monotonicity property.
Therefore, the function satisfies the boundary condition.
We observe that the function is a t-conorm.
In addition, the function is continuous because it is continuous in both arguments.
The function is Archimedean if .
Suppose that
applies because the t-norm
T is Archimedean.
The function is strict because it is continuous and strictly monotonous.
The function is nilpotent because, if is continuous and Archimedean, then there exist some such that .
Ιndeed,
applies, because the t-norm
T is continuous, strict and Archimedean; therefore, there are
such that
(see Klement E.P., Theorem 2.18, p. 33, [
7]). □
Theorem 5 presents the general form of t-conorms using an automorphism function, according to the equation
(see Gottwald S., Proposition 5.3.1, p. 90, [
6]). Formula (
5) can be used to generate new t-conorms (see Example 1v).
Theorem 5. If there exists a continuous (Archimedean, strict, nilpotent) t-conorm , a (strong negation) , a continuous (Archimedean, strict, nilpotent) t-norm and an automorphism φ such that it is defined by then is a continuous (Archimedean, strict, nilpotent) t-conorm.
Proof of Theorem 5. From (Gottwald S., Proposition 5.3.1, p. 90, [
6]),
Therefore, the function
satisfies the commutativity property.
Therefore, the function
satisfies the associativity property.
Therefore, the function
satisfies the monotonicity property.
Therefore, the function satisfies the boundary condition.
We observe that the function is a t-conorm.
In addition, the function is continuous because it is continuous in both arguments.
The function is Archimedean if applies.
Suppose that
applies because the t-norm
T is Archimedean.
The function is strict because it is continuous and strictly monotonous.
The function is continuous and Archimedean, so it is nilpotent. Therefore, some exist such that .
Ιndeed,
applies because the t-norm
T is continuous, strict and Archimedean; therefore,
such that
exist (see Klement E.P., Theorem 2.18, p. 33, [
7]). □
Theorem 6 presents the general form of I-implications using an automorphism function, according to the equation
(see Corollary 2.5.31, p. 87, [
22]). Formula (
6) can be used to generate new I-implications (see Example 1vi).
Theorem 6. If there exists a function , a strong negation , a t-norm and an automorphism φ such that the function is fuzzy implication is defined by: Proof of Theorem 6. Therefore, the function satisfies the property ().
Therefore, the function satisfies the property ().
Therefore, the function satisfies the property ().
Therefore, the function satisfies the property ().
Therefore, the function satisfies the property ().
Consequently, the function satisfies the properties of the family of fuzzy implications.
The set of all fuzzy implications will be denoted by . □
Example 1. Let f be a automorphism function ,
The function f is a strictly increasing in with .
(i). Let N be a strong fuzzy negation of the Sugeno class:
From Formula (1) of Theorem 1: (ii). Let be a strict t-norm .
From Formula (2) of Theorem 2: (iii). Let be a strict t-conorm .
From Formula (3) of Theorem 3: (iv). Alternatively, the S-conorm can be defined from Formula (4) of Theorem 4: (v). In addition, the S-conorm can be defined from Formula (5) of Theorem 5: (vi). Let N be a strong fuzzy negation of the Sugeno class , and be a strict t-norm .
From Formula (6) of Theorem 6: (i). It is easy to see that a function defined by (
7) is an involution with the following properties:
and
. It is also strictly decreasing. Hence,
is a strong negation function.
(ii). It is easy to see that a function defined by (
8) is a strict and Archimedean t-norm. The function
is commutative and associative and it satisfies the boundary condition.
(iii). It is easy to see that a function defined by (
9) is a strict and Archimedean t-conorm. The function is commutative, associative and monotonous and it satisfies the boundary condition.
The graph is shown below.
(iv). It is easy to see that a function defined by (
10) is a strict and Archimedean t-conorm. The function
is commutative, associative and monotonous and it satisfies the boundary condition.
The graph is shown below.
(v). It is easy to see that a function defined by (
11) is a strict and Archimedean t-conorm.
The function is commutative, associative and monotonous and it satisfies the boundary condition.
The graph is shown below.
Remark 1. Figure 4, Figure 5 and Figure 6 are observed to have the same graph. Therefore, we conclude that the S t-conorms given by Theorems 3–5 express the same S t-conorm. (vi). The function satisfies the properties of the family of fuzzy implications.
6. Discussion
The field of research of fuzzy connectives has been explored by multiple researchers over the years. As a result, multiple strategies for generalizing fuzzy connectives have been discovered. This paper focused on their limitations and provided solutions, which resulted in the creation of a new strategy. The various applications of this new method, as well a their results, are visible in the following paragraphs.
To be more specific, fuzzy connectives using the natural negation have been generated in the past (see J.C. Fodor and M. Roubens, Theorem 1.1, p. 4, [
14]), (Gottwald S., Theorem 5.2.1 p. 86, [
6]) and (Fodor J., p. 2077, [
19]). However, the limitation is that this strategy involves only the natural negation in the process of generalizing the fuzzy connectives. The strategy presented in this paper, though, is capable of replacing the natural negation with any strong negation. This allows for the creation of new fuzzy connectives capable of involving all negations in the process of generalization.
Furthermore, fuzzy connectives using the T-Minimum, T-Product and T-Lukasiewicz t-norms have been generated in the past (see René B. et al., Theorem 2.3, p. 372, [
20]). In addition, Gottwald S., Theorem 5.1.3, p. 82, [
6] worked with such functions, but they focused mainly on the specific forms of t-norms (see
Table 4). However, the limitation is that this strategy involves only these specific t-norms in the process of generalizing the fuzzy connectives. The strategy presented in this paper, though, is capable of replacing the T-Minimum, T-Product and T-Lukasiewicz t-norms with any t-norm. This allows for the creation of new fuzzy connectives capable of involving all t-norms in the process of generalization.
Moreover, this paper presents the generalization of fuzzy connectives using S-conorms. The prospect of incorporating S-conorms in the process of generalizing fuzzy connectives has not been explored in the past. In order to achieve this, the new strategy is based on the strategies mentioned before.
In addition, a strategy employing S-conorms, t-norms as well N-negations in the process of generalizing fuzzy connectives is explored in this paper. Such a strategy has not been implemented by someone else before.
Finally, a strategy for generalizing the classes of the I-implications was discovered in the past (see Bustince H., Burillo P. and Soria F. in 2003 ( [
17]). Callejas C., Marcos J. and Bedregal B., in 2012, created the fifth strategy (see
Figure 8), which generates any fuzzy implication ([
18]). In this paper, however, a new method of generalizing I- implications with a combination of N-negations and t-norms is presented. This method will play a crucial role in future research, as it allows for the generalization of I-implications, which, in conjunction with weather data, can provide a better understanding of climate change.