Abstract
This study introduces the concept of an intuitionistic fuzzy SBCK-subalgebra (SBCK-ideal) and explores the level set of an intuitionistic fuzzy set within the context of Sheffer stroke BCK-algebras. These newly defined concepts are crucial for understanding the interaction between intuitionistic logic and Sheffer stroke BCK-algebras. The paper establishes a connection between subalgebras and level sets in the framework of Sheffer stroke BCK-algebras, demonstrating that the level set of intuitionistic fuzzy SBCK-subalgebras corresponds precisely to their subalgebras, and conversely. Additionally, the study provides novel results regarding the structural properties of Sheffer stroke BCK-algebras under intuitionistic fuzzy logic, specifically focusing on the conditions under which fuzzy sets become SBCK-subalgebras or SBCK-ideals. This work contributes to the theoretical foundations of fuzzy logic in algebraic structures, offering a deeper understanding of the interplay between intuitionistic fuzzy sets and the algebraic operations within Sheffer stroke BCK-algebras.
Keywords:
Sheffer stroke (BCK-algebra); BCK-ideal; intuitionistic fuzzy SBCK-subalgebra; intuitionistic fuzzy SBCK-ideal MSC:
06F05; 03G25; 03G10
1. Introduction
The concept of a fuzzy set was first introduced by Zadeh [1] in 1965, marking the beginning of a new era in set theory and logic. Following this, various extensions of the fuzzy set were proposed to address different complexities. The first extension was the L-fuzzy set, introduced by Goguen [2] in 1967, which expanded the scope of fuzzy sets. The second extension, introduced by Zadeh [3], was the interval-valued fuzzy set, which allowed for more nuanced representations of uncertainty. The third extension, known as the rough set, was defined by Pawlak in 1981 [4,5], providing a framework for handling approximation and uncertainty in data. The fourth extension, and perhaps one of the most influential, was the intuitionistic fuzzy set, introduced by Atanassov in 1983 [6,7,8], which added an additional layer of membership and non-membership, providing a more flexible approach to handling vagueness and uncertainty in various applications.
The Sheffer operation, also called the Sheffer stroke or NAND operator, was introduced by H. M. Sheffer [9]. It is highly significant because it can define an entire logical system by itself, enabling any axiom within that system to be expressed solely in terms of the Sheffer operation. This ability provides a distinct advantage in controlling and simplifying the logical structure. Interestingly, even the axioms of Boolean algebra, the algebraic counterpart of classical propositional calculus, can be fully expressed using only the Sheffer operation, highlighting its essential role in logical and algebraic frameworks.
The Sheffer stroke is critical because it allows all Boolean functions to be represented with a single operation, making it a powerful and efficient tool in Boolean algebra. This simplification reduces the need for multiple operations, streamlining logical systems. Beyond Boolean algebra, the Sheffer stroke has been applied in numerous other algebraic structures, such as MV-algebras, BL-algebras, BCK-algebras, BE-algebras, ortholattices, and Hilbert algebras, demonstrating its broad applicability across different areas of mathematics.
The motivation for this study arises from the intersection of two powerful concepts in mathematical logic: the Sheffer stroke operation and intuitionistic fuzzy set theory. While Sheffer stroke BCK-algebras have been studied for their foundational role in algebraic logic, the incorporation of intuitionistic fuzzy structures into these algebras remains relatively unexplored. Intuitionistic fuzzy sets provide a nuanced way to represent uncertainty and partial truth, which is crucial for modern applications in decision making, control theory, and artificial intelligence. By defining and analyzing intuitionistic fuzzy SBCK-subalgebras and ideals, this paper aims to establish a novel theoretical framework that enhances the expressive power of BCK-algebras under the Sheffer operation. This contributes to both the enrichment of algebraic theory and the potential for practical applications in areas requiring fuzzy logic with structured algebraic underpinnings.
Initially presented by Sheffer, the Sheffer stroke has since become a cornerstone in Boolean algebra due to its ability to replace all standard Boolean operations. Its usefulness extends beyond Boolean algebras, with applications in basic algebras and ortholattices, among others (see [10,11]). One of the relevant studies on this topic is Intuitionistic Fuzzy Structures on Sheffer Stroke UP-algebras [12].
Recent developments in mathematical logic and dynamical systems, such as studies on bi-center problems, isochronous centers in switching systems, and nilpotent center conditions [13,14,15], offer valuable insights into complex structures. Although focused on dynamical systems, these works share a common goal with the present study: advancing the understanding of structured mathematical models through detailed analysis.
In this paper, we introduce the concept of an intuitionistic fuzzy SBCK-subalgebra and examine the level set of an intuitionistic fuzzy set within the context of Sheffer stroke BCK-algebras. These newly defined concepts play a crucial role in understanding the behavior of intuitionistic logic within the framework of Sheffer stroke BCK-algebras. The study establishes a connection between subalgebras and level sets, demonstrating that the level set of an intuitionistic fuzzy SBCK-subalgebra in this algebra is precisely its subalgebra, and vice versa. This relationship underscores the close connection between these two concepts within the given algebraic structure. Furthermore, the paper defines the notion of an intuitionistic fuzzy SBCK-ideal in a Sheffer stroke BCK-algebra and explores its properties. It is also shown that every intuitionistic fuzzy SBCK-ideal of a Sheffer stroke BCK-algebra is, in fact, an intuitionistic fuzzy SBCK-subalgebra; however, the reverse is generally not true. This distinction highlights the unique characteristics and behavior of intuitionistic fuzzy SBCK-ideals within the context of Sheffer stroke BCK-algebras.
The rest of this paper is organized as follows: Section 2 introduces the basic definitions and preliminaries related to Sheffer stroke BCK-algebras and intuitionistic fuzzy sets. In Section 3, we define the concept of intuitionistic fuzzy SBCK-subalgebras and explore their structural properties, followed by the examination of their corresponding level sets. Section 4 focuses on the notion of intuitionistic fuzzy SBCK-ideals, providing a comprehensive analysis of their relationship with SBCK-subalgebras. Finally, Section 5 concludes the paper by summarizing the findings and suggesting potential areas for future research.
The list of acronyms is given in Table 1.
Table 1.
List of acronyms.
2. Preliminaries
Definition 1
([9]). Let be a groupoid. The operation | is said to be a Sheffer stroke operation if it satisfies the following conditions:
- (S1)
- (S2)
- (S3)
- (S4)
for all .
As anotational convenience, we denote
Definition 2
([16]). A Sheffer stroke BCK-algebra (briefly, SBCK-algebra) is a structure of type (2) such that 0 is the fixed element in X and the following conditions are satisfied for all :
Proposition 1
([16]). Let be an SBCK-algebra. Then, the binary relation if and only if is a partial order on X.
Definition 3
([16]). Let be an SBCK-algebra. A nonempty subset S of X is called an SBCK-subalgebra of X if for all .
Proposition 2
([16]). et be an SBCK-algebra. A nonempty subset I of X is called an SBCK-ideal of X if for all .
- 1.
- ,
- 2.
- and ⇒.
Lemma 1
([17]). Let μ be a fuzzy set in a nonempty set H and . Then,
- 1.
- ,
- 2.
- .
Intuitionistic fuzzy sets (IFSs), introduced by Atanassov, extend classical fuzzy sets by incorporating both a membership function and a non-membership function , where for all . The degree of indeterminacy, , quantifies uncertainty or indecision. The primary condition is that , ensuring a degree of indeterminacy.
Definition 4
([6]). Let H be a nonempty set. The intutionstic fuzzy set A on H is defined to be a structure
where is the degree of membership of to A and is the degree of non-membership of to A such that .
IFSs have been widely applied in areas such as decision making, pattern recognition, and control systems, where uncertainty plays a significant role. Recently, the integration of IFSs into algebraic structures like BCK-algebras has led to the development of new concepts such as intuitionistic fuzzy SBCK-subalgebras and SBCK-ideals, which are explored in the following sections.
Lemma 2
([17]). Let . Then the following statements hold:
- 1.
- ,
- 2.
- .
3. Intuitionistic Fuzzy SBCK-Subalgebras
In this section, the study introduces the concept of intuitionistic fuzzy SBCK-subalgebras within the context of -algebras. It is important to note that, unless explicitly stated otherwise, X refers to a Sheffer stroke BCK-algebra.
Definition 5.
An in X is called an intuitionistic fuzzy SBCK-subalgebra (briefly, -subalgebra) of X if
Example 1.
Let be a set with the binary operation “” given in the following table:
| | | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 1 | 1 | 0 | 3 | 2 | 5 | 4 | 7 | 6 |
| 2 | 1 | 3 | 3 | 1 | 1 | 3 | 3 | 1 |
| 3 | 1 | 2 | 1 | 2 | 5 | 6 | 5 | 6 |
| 4 | 1 | 5 | 1 | 5 | 5 | 1 | 5 | 1 |
| 5 | 1 | 4 | 3 | 6 | 1 | 4 | 3 | 6 |
| 6 | 1 | 7 | 3 | 5 | 5 | 3 | 7 | 1 |
| 7 | 1 | 6 | 1 | 6 | 1 | 6 | 1 | 6 |
Then, is a -algebra. Define in X by the table below:
| X | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
It is routine to verify that the in X is a -subalgebra of X.
Definition 6.
Let μ be a fuzzy set on an -algebra X and let . We define the following two subsets of X:
- The upper level set of μ at level is defined as
- The lower level set of μ at level is defined as
These sets are referred to as the level sets of the fuzzy set μ and play an important role in the study of fuzzy subalgebras in -algebras.
Example 2.
Let be the given in Example 1. For , we have
For , we have
Theorem 1.
An in X is an -subalgebra of X if and only if the sets and are SBCK-subalgebras of X whenever they are nonempty for all .
Proof.
Assume that is an -subalgebra of X and for all . Let be such that and . Then, , , and . Then, and and so . Therefore, and are SBCK-subalgebras of X.
Conversely, let be an in X for which and are SBCK-subalgebras of X whenever they are nonempty for all . Suppose that or for some . Then, we have or where and . However, or , a contradiction. Therefore, we obtain and for all . Consequently, is an -subalgebra of X. □
Theorem 2.
An in X is an -subalgebra of X if and only if the fuzzy sets and ϱ are fuzzy SBCK-subalgebras of X, where is defined by for all .
Proof.
Assume that is an -subalgebra of X. It is clear that is a fuzzy SBCK-subalgebra of X. For every ,
Hence, is a fuzzy SBCK-subalgebra of X.
Conversely, let be an of X for which and are fuzzy SBCK-subalgebras of X. Let . Then
Hence, is an -subalgebra of X. □
Theorem 3.
Given a nonempty subset Y of X, let be an IFS in X defined as follows:
and
for all and such that , , and for . Then, be an -subalgebra of X if and only if Y is a SBCK-subalgebra of X.
Proof.
Assume that is an -subalgebra of X. Let be such that . Then,
and
and so and . This shows that . Therefore, Y is a SBCK-subalgebra of X.
Conversely, let Y be a SBCK-subalgebra of X. For every , if , then, , which implies that and . If of , then we have and . Therefore, is an -subalgebra of X. □
Theorem 4.
An IFS in X is an -subalgebra of X, then, and for all .
Proof.
For any ,
and
□
Proposition 3.
For an -subalgebra of X satisfies
if and only if and .
Proof.
We have
and
Then, by Theorem 4, we have and . The converse is clear. □
4. Intuitionistic Fuzzy Ideals in SBCK-Algebras
In this section, the study presents the introduction of intuitionistic fuzzy SBCK-ideals within the framework of Sheffer stroke BCK-algebras. By defining these new concepts, we aim to expand the understanding of the interplay between intuitionistic fuzzy logic and -algebraic structures. The exploration of intuitionistic fuzzy SBCK-ideals provides a foundational perspective for further analysis of their properties and their role in the broader context of algebraic logic.
Definition 7.
An on X is called an intuitionistic fuzzy SBCK-ideal (briefly, -ideal) of X if
Example 3.
Let be a set with the binary operation “∣” given in the following table:
| | | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 1 | 1 | 0 | 3 | 2 | 5 | 4 | 7 | 6 |
| 2 | 1 | 3 | 3 | 1 | 6 | 4 | 4 | 6 |
| 3 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 1 |
| 4 | 1 | 5 | 6 | 2 | 5 | 1 | 2 | 6 |
| 5 | 1 | 4 | 4 | 1 | 1 | 4 | 4 | 1 |
| 6 | 1 | 7 | 4 | 2 | 2 | 4 | 7 | 1 |
| 7 | 1 | 6 | 6 | 1 | 6 | 1 | 1 | 6 |
Then, is a -algebra. Define the in X by the table below:
| X | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
It is routine to verify that the in X is an -ideal of X.
Lemma 3.
If is an of X, then
Proof.
Let be an of X and . Then, by Theorem 4, we have
and
for all . □
Theorem 5.
Let be an of X. Then, is an of X if and only if
for all
Proof.
Let be an of X and . Then,
and
We have
and
Conversely, let be an of X satisfies the condition (6). Since
we have and for all . Since , we obtain . Since , we obtain and for all . Thus, is an of X. □
Theorem 6.
Every -ideal of X is an -subalgebra of X.
Proof.
Let be an -ideal of X. Then,
and
Hence, is an of X. □
Theorem 7.
An in X is an -ideal of X if and only if the sets and are SBCK-ideals of X whenever they are nonempty for all .
Proof.
Assume that is an of X and for all . Let be such that . Then and . Then, and and so . Let be such that and . Then, , , and . Then, and and so . Therefore, and are SBCK-ideals of X.
Conversely, let be an in X for which its negative -cut and positive -cut are SBCK-ideals of X whenever they are nonempty for all . Suppose that for some . Then, but , a contradiction. Hence, for all . Suppose that for some . Then, but , a contradiction. Hence, for all . Assume that or for some . Then, or where and . However, or , a contradiction. Therefore, and for all . Consequently, is an -ideal of X. □
Theorem 8.
An in X is an -ideal of X if and only if the fuzzy sets and ϱ are fuzzy SBCK-ideals of X, where defined by .
Proof.
Assume that is an -ideal of X. It is clear that is a fuzzy SBCK-ideal of X. For every ,
and
Hence, is a fuzzy SBCK-ideal of X.
Conversely, let be an of X for which and are fuzzy SBCK-ideals of X. Let . Then,
so
and
so
Hence, is an -ideal of X. □
Theorem 9.
Given a nonempty subset Y of X, let be an in X defined as follows:
and
where in and in . Then be an -ideal of X if and only if Y is a SBCK-ideal of X.
Proof.
Assume that is an -ideal of X. Let be such that . Then,
and so and . This shows that . Then,
and so and . This shows that . Therefore, F is a SBCK-ideal of X.
Conversely, let Y be a SBCK-ideal of X. For every , if , then which implies that and . If , then and . For every , if , then , which implies that
and
If of , then and . Therefore, is an -ideal of X. □
Proposition 4.
If , where Δ is an arbitrary index set, is a family of -ideals of X, then is an -ideal of X.
Proof.
Let be a family of -ideals of a SBCK-algebra X.
Let . Then,
and
Let . Then
and
Hence, is an -ideal of a SBCK-algebra X. □
Definition 8
([16]). Let and be SBCK-algebras. Then, a mapping is called a homomorphism if for all and .
Let be a groupoid. For every element , consider the following mapping:
Theorem 10.
Let and be SBCK-algebras, be a surjective homomorphism and be an on Y. Then, is an -ideal of Y if and only if is an -ideal of X.
Proof.
Let and be SBCK-algebras, be a surjective homomorphism and be an -ideal of Y. Let . Then,
and
and
and
Hence, is an -ideal of X.
Conversely, let be an -ideall of X. Let such that and for . Then,
and
and
and
Hence, is an -ideal of Y. □
Theorem 11.
Let and be SBCK-algebras, be a surjective homomorphism and be an on Y. Then, is an -subalgebra of Y if and only if is an -subalgebra of X.
Proof.
and
Hence, is an -subalgebra of X.
Let and be SBCK-algebras, be a surjective homomorphism and be an -subalgebra of Y. Let . Then,
Conversely, let be an -subalgebra of X. Let such that and for . Then,
Hence, is an -subalgebra of Y. □
5. Conclusions
In this study, we have introduced and systematically examined the structure of intuitionistic fuzzy SBCK-subalgebras and SBCK-ideals within the framework of SBCK-algebras. Our primary contributions center on characterizing these intuitionistic fuzzy structures through their corresponding level sets and associated fuzzy subsets.
We first established that an intuitionistic fuzzy set (IFS) in an SBCK-algebra is an -subalgebra (or -ideal) if and only if its upper and lower level sets form SBCK-subalgebras (or ideals), providing a fundamental link between intuitionistic fuzzy structures and classical algebraic substructures. Additionally, we demonstrated that such an IFS can be equivalently described in terms of its component fuzzy sets, particularly the transformed non-membership function , which must itself satisfy the fuzzy subalgebra (or ideal) conditions.
A further key result showed that if an IFS is defined via characteristic functions on a subset , then it forms an -subalgebra (or ideal) if and only if Y is itself an SBCK-subalgebra (or ideal). This provides a constructive method for generating intuitionistic fuzzy substructures. We also identified several necessary conditions that such fuzzy structures must satisfy, including bounds on the membership and non-membership functions at the zero element of the algebra and their behavior under the algebra’s partial order.
Moreover, we confirmed that every -ideal is necessarily an -subalgebra, reflecting a hierarchical structure within these intuitionistic fuzzy systems. Closure properties under intersection were also established, ensuring the internal consistency of the class of -ideals.
Finally, we extended our findings to the homomorphic image of intuitionistic fuzzy SBCK-subalgebras and ideals, showing that such structures are preserved under surjective homomorphisms. This result broadens the applicability of our framework to categorical and structural studies in algebra.
Future research could also investigate the application of intuitionistic fuzzy SBCK-subalgebras to problems such as center and isochronous center conditions in switching systems [18], complex isochronous centers in -equivariant planar systems [19], and the center-focus classification in generalized cubic Kukles systems with nilpotent singular points [20]. These connections may open new pathways for applying fuzzy algebraic methods to the qualitative analysis of dynamical systems.
Future research could focus on extending the current results to more general algebraic systems or different classes of non-classical logics. One potential avenue is to investigate the properties of intuitionistic fuzzy SBCK-subalgebras within other algebraic structures, such as lattice-based or non-commutative algebras. Moreover, computational approaches for handling these structures in practical applications, such as fuzzy decision making and reasoning, could be explored further. Additionally, a deeper investigation into the relationships between intuitionistic fuzzy sets and other logical operations within the broader context of BCK-algebras may yield new insights and open up new avenues for research in both algebraic theory and fuzzy logic applications.
Author Contributions
Conceptualization, T.O., R.N., R.K.B. and A.S.A.; methodology, T.O., R.N., R.K.B. and A.S.A.; writing— T.O., R.N., R.K.B. and A.S.A.; writing—review and editing, T.O., R.N., R.K.B. and A.S.A.; visualization, T.O., R.N., R.K.B. and A.S.A.; supervision, T.O., R.N., R.K.B. and A.S.A.; funding acquisition, A.S.A. All authors have read and agreed to the published version of the manuscript.
Funding
This research is funded by Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia under Researchers Supporting Project Number (PNURSP2025R231).
Data Availability Statement
Data sharing is not applicable as no datasets were generated or analyzed during the current study.
Acknowledgments
The authors are very thankful to the anonymous referees for their valuable comments and suggestions which have improved the manuscript immensely. Moreover, the authors extend their appreciation to Princess Nourah bint Abdulrahman University (PNU), Riyadh, Saudi Arabia, for funding this research under the Researchers Supporting Project, No. PNURSP2025R231.
Conflicts of Interest
The authors have no competing interests to declare that are relevant to the content of this article.
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