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For a commutative unital quantale L and a strong L-fuzzy topological space X, we define the open-hood
Introduction
We live in an information explosion world since a large amount of information appears in our everyday life and the rapid development of information science, computer science with the maturity of network technology. We always deal with information and design related algorithms from given massive data by using theory and methods of information science, computer science and applied mathematics. Among them, information granulation and granular computing are very effective methods to various challenges, and have received much attention from a lot of fields, such as computational intelligence, clustering analysis, Dempster–Shafer theory, machine learning, rough set theory, etc.
Information granulation and granular computing were presented by Zadeh (1979, 1997) by making use of fuzzy set theory. He thought that human cognition was mainly composed of three concepts: granulation, organization and causality. Yager and Filev (1998) suggested to develop a general method to granular computing as one of the main tasks in this field. Various granular computing models can be obtained by reformulating re-interpreting and combining from rough sets (Ma et al. 2007; Pawlak 1982, 1991; Yao 2004), quotient spaces (Zhang and Zhang 2005), belief functions (Shafer 1976), and word computing (Wang and Lin 1998; Wang 2001; Zadeh 1996), etc.
Rough set theory was initially introduced and studied by Pawlak (1982, 1991), providing a systematic approach and method to object classification via different kinds of binary relations and has wide applications in a lot of engineering fields. Mathematically speaking, rough sets were taken its rise from the classification problem with equivalence relations and partition as the fundamental structures. Nevertheless, the traditional rough set theory can no longer handle certain kinds of granular problems since it is hard for us to get a very reasonable equivalence relation from a data set. We then need some of general binary relation (Skowron and Stepaniuk 1996; Zheng et al. 2005; Slowinski and Vanderpooten 2000) or fuzzy types of binary relations (Dubois and Prade 1990; Liu and Sai 2010; Mi and Zhang 2004; Morsi and Yakout 1998; Wu et al. 2005, 2016) to build rough set models. While except for binary relations, the notion of neighborhood operators/systems and that of coverings is other useful tools to construct rough set models (Yao 1998, 1999; Zhu 2009), which can be considered as different generalizations of granule-based models. It should be mentioned that various distance functions are a further kind of structures to construct and study roughness by means of fuzzy set theory (Yao et al. 2019, 2023).
In a broad sense, all of relation-based, neighborhood operators/systems-based, covering-based and even distance function-based rough set models are indeed topological models, since the former three are based on powersets and certain subfamilies, and the latter one is strongly related with metric topology. By this regard, in Yao and Han (2023) we introduced a topological model of granular computing by defining the notions of open/closed-hoods and zooming-in/out operators for Alexandrov spaces (Alexandroff 1937; Miller et al. 2010). This idea in fact has already been stated showing close relations among ordered structures, rough set structures and topological structures in Steiner (1966), Skowron (1988), Wiweger (1988), Pawlak (1991), SP, Srivastava AK (2013), Perfilieva et al. (2018), Perfilieva et al. (2020), Perfilieva (2022) and Sostak and Uljane (2023).
The topological model in Yao and Han (2023) can be considered as a common framework of the preordered relation model, the Alexandrov neighborhood operator model and the covering model. In this paper we would like to follow the step of Yao and Han (2023) to establish a rough set model of lattice-valued fuzzy Alexandrov topology, and wish to supply a common framework of the lattice-valued fuzzy preordered relation model, certain types of lattice-valued fuzzy neighborhood operator model and certain types of lattice-valued fuzzy covering model.
The structure of this paper is arranged as follows. For a commutative unital quantale L and a strong L-fuzzy topological space X, we define the open-hood for every point . Considering as the family of basic granules, we define the upper and lower approximation operators . It is shown that are a kind of a closure operator and an interior operator respectively, and form an L-Galois connection on . By introducing notions of one zooming-in operator and two zooming out operators, we study the composition and decomposition problems of .
Preliminaries
In this section, we recall some basic concepts and results on quantales, lattice-valued topology and fuzzy posets.
Definition 1
Rosenthal (1990) A commutative unital quantale is a triple such that
(Q1) L is a complete lattice with 0, 1 as the bottom and the top element respectively;
(Q2) is a commutative monoid with e as the unit;
(Q3) the operation is distributive over joins of arbitrary subsets of Q, that is,
By lattice theory, the operation can induce an implication operation satisfying that .
Example 1
Let denote the extended interval of all non-negative real numbers with the same ordering as the real numbers. Let be the usual multiplication on real numbers extended to cope with the infinity such that for every and . Then is a commutative unital quantale.
Let denote the extended interval of all non-negative real numbers with the opposite ordering as real numbers (so 0 is the greatest element). Let be the usual addition on real numbers extended to cope with infinity, such that for every . Then is a commutative unital quantale.
The unit interval [0, 1] equipped with a left continuous t-norm (e.g., the usual , and ) becomes a commutative unital quantale.
Proposition 1
(Rosenthal 1990) Suppose that is a commutative unital quantale. Then
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Definition 2
Yao and Lu (2009) Let X be a nonempty set and let be a mapping. The pair (X, p) or just X, is called an L-preordered set if
Self-reflexivity: ;
Transitivity:
Example 2
Let by . Then is an L-preordered set.
Define by . Then is an L-preorder on .
An L-subset A of an L-preordered set (X, p) is called a lower set (resp., an upper set if (resp., ). Denote by Low(X) (resp., Upp(X)) the family of all lower sets (resp., upper sets) of X.
Definition 3
Yao and Lu (2009) Suppose that and are two monotone mappings between two L-preordered sets. The pair (f, g) is called an L-Galois adjunction from P to Q if
Example 3
Let be a mapping. Then is an L-Galois adjunction from to , where and are respectively defined by
Definition 4
Chen and Zhang (2010) Suppose that X is a nonempty set. A strong L-fuzzy Alexandrov topology on X is a family of L-subsets of X satisfying that
;
;
.
For every L-preordered set X, it is easy to show that both Low(X) and Upp(X) are strong L-fuzzy Alexandrov topologies on X.
Rough approximation operators of strong L-fuzzy Alexandrov spaces
In this section, the pair always denotes a strong L-fuzzy Alexandrov space.
For each , define by
Theorem 2
For every , is the minimal open set such that , called the open-hood of x.
Proof
By (A2, A3), it is easy to show that and . Suppose there exists such that . Then for every , it holds that , and therefore .
Define two operators by: ,called the upper approximation operator and the lower approximation operator of X respectively.
Remark 1
By Theorem 2, for every , the subset is the minimal open set containing x. This feature is also possessed by the equivalence classes in Pawlak approximation space, namely for an equivalence relation on a set X and , the equivalence class [x] is the minimal definable set containing x. In this sense, the family in a strong L-fuzzy Alexandrov space can be considered as a kind of fuzzy topological types of approximation spaces.
In the non-fuzzy setting, for an Alexandrov topology X and , the subset can be written as and the subset can be written as . By (1), and are just the upper and lower approximations of A respectively. In this sense, the pair is a kind of fuzzy topological generalizations of the classical approximation operators.
In the following, we will study the properties of the operators . Firstly, let us prove a very useful lemma.
Lemma 1
Proof
Firstly, since , we have , and then , which means . Conversely,and thus . Hence, .
Secondly, on one hand we haveand then . On the other hand,Hence, .
Theorem 3
The pair is an L-Galois adjunction on .
Proof
For all , it holds that
Theorem 4
Let be a strong L-fuzzy Alexandrov space. Then
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Proof
(Cl1) Clearly . For every and every ,Then .
(Cl2) For every and , we haveThen .
(Cl3) For every and , we haveHence, .
(Cl4) For every and , we haveHence, .
(Cl5) For every and , we haveHence, .
(Cl6) For all , we have
Theorem 5
Let be a strong L-fuzzy Alexandrov space. Then
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Proof
(Int1) Clearly . For every and every ,Then .
(Int2) For every and , we have(Int3) For every and , we haveHence, (Int4) For every and , we haveHence, .
(Int5) For every and , we haveHence, .
(Int6) For all , we have
Theorem 6
Let be a strong L-fuzzy Alexandrov space.
;
.
Proof
The first equality in each of (1–2) is just (Cl4) and (Int4) respectively. For the second equality, we give the following proof.
(1) By (Int2), we have . Thus we only need to prove . In fact, for any and , we haveHence,
(2) By (Cl2), we have . Thus we only need to prove .
In fact, for any we haveHence we have
Theorem 7
The following statements are equivalent:
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Proof
By the definition of , (2) is equivalent to (4).
(1) (2) Firstly, by (Int2), it is obviously . Secondly, for every , we haveThus .
(2) (3) By and , we have .
(3) (1) For every , we have and then . By (A2), .
Remark 2
For an L-fuzzy topological space , the related interior operator is computed as (Höhle and Sostak 1999). We can show, for a strong L-fuzzy Alexandrov topological space , .
Proof
For every and every ,Conversely, for every with , we haveBy the arbitrariness of B, . Hence, .
It has been explained in the last section of Yao and Han (2023) that: for a fuzzy Alexandrov topology , it would be hard to get some similar results as those in Yao and Han (2023) unless . We now will apply this condition on the commutative unital quantale L to get a dual result of Remark 2.
A commutative unital quantale called Girard if there is an element such that holds for all . The element d is called a dual cyclic element.
Proposition 8
Let L be a Girard commutative unital quantale with d as a dual cyclic element of L. Denote . Then for all and all , it holds that:
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Proof
Since L is Girard and d is a dual cyclic element, we have .
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Remark 3
For an L-fuzzy cotopological space , the related closure operator is computed as . We can show, for a strong L-fuzzy Alexandrov topological space , .
Proof
For all , denote . For every , it easily seen that and then we would like to show that . In fact, for every , we haveThus , as desired.
ThenConversely, for every with ,By the arbitrariness of , we have . Hence, .
Composition and decomposition of rough approximation operators
As is well known, the upper and lower approximations of classical sets are also subsets of the same universes. From the definitions of zooming-in and zooming-out operations, we can combine them to derive some new operators in the same universes. In fact, not only we can obtain approximation operators of a reflexive approximation space, but we can also obtain approximation operators of a granulated universe of discourse. In this section, we will study composition and decomposition problems of rough approximation operators of strong L-fuzzy Alexandrov spaces.
Let be a strong L-fuzzy Alexandrov space and letThen is preordered set under the set-inclusion.
Define a zooming-in operator byDefine two zooming-out operators by
Theorem 9
Let be a strong L-fuzzy Alexandrov space. Then
;
;
;
.
Proof
Let , .
Theorem 10
Let . Then if and only if that there is a lower set P of such that .
Proof
The necessity: Let . We havewhich indicates P is a lower set of .
The sufficiency: By Theorem 7, we only need to prove . On one hand,On the other hand, since P is a lower set of and , we have and then . ThusHence, .
Lemma 2
Let (X, p) be an L-preordered set and A is a lower set. Then for every ,
By Lemma 2, for a strong L-fuzzy Alexandrov space and for every lower set A of , we have
Theorem 11
The operators are respectively the left and right adjoint of for and .
Proof
Let P be a lower set of and . Thenand
Conclusions and further work
For a commutative unital quantale L and a strong L-fuzzy Alexandrov topological space X, we defined the open-hood for every point . Considering as the family of basic granules, we defined the upper and lower approximation operators . It is shown that are a kind of a closure operator and an interior operator respectively, and form an L-Galois connection on . By introducing notions of one zooming-in operator and two zooming out operators, we studied the composition and decomposition problems of .
We have some further work:
In Yao and Han (2023), by an Alexandrov space and by using the open-hoods and closed-hoods, we define four rough approximation operators, while in this paper, on two rough approximation operators related to the open-hoods have been defined for strong L-fuzzy Alexandrov topological space. We can further define the closed-hood for every point, define another two rough approximation operators and then study the mutual interactions among them.
It is shown in Yao and Han (2023), rough set model of classical Alexandrov topology can be considered as a common framework of the preordered relation model, the neighborhood operator model and the covering model. We can further study this problem, to show that the rough set model of strong L-fuzzy Alexandrov topology can be considered as a common framework of the L-preordered relation model, certain types of neighborhood operator model and certain types of the covering model.
Acknowledgements
This paper is supported by the NNSF of China (1237146, 12231007), Jiangsu Provincial Innovative and Entrepreneurial Talent Support Plan (JSSCRC2021521).
Author Contributions
All authors contributed to the study conception and design. All authors read and approved the final manuscript.
Funding
Funding was supported by National Natural Science Foundation of China (1237146, 12231007), Jiangsu Provincial Innovative and Entrepreneurial Talent Support Plan (JSSCRC2021521).
Data availability
All data, models, or code generated or used during the study are available from the corresponding author by request.
Declarations
Conflict of interest
The authors have no conflict of interest to declare that are relevant to the content of this article.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
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Informed consent was obtained from all individual participants included in the study.
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References
Alexandroff, P. Diskrete Räume, Matematicheskii. Sbornik; 1937; 2, pp. 501-519.
Chen, P; Zhang, D. Alexandroff -cotopological spaces. Fuzzy Sets Syst; 2010; 161, pp. 2505-2514. [DOI: https://dx.doi.org/10.1016/j.fss.2010.01.002]
Dubois, D; Prade, H. Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst; 1990; 17,
Höhle, U; Sostak, AP. Höhle, U; Rodabaugh, SE. Axiomatic foundations of fixed-basis fuzzy topology. Mathematics of fuzzy sets (topology and measure theory); 1999; Boston, Kluwer Academic Publishers: pp. 123-272. [DOI: https://dx.doi.org/10.1007/978-1-4615-5079-2_5]
Liu, GL; Sai, Y. Invertible approximation operators of generalized rough sets and fuzzy rough sets. Inf Sci; 2010; 180,
Ma, JM; Zhang, WX; Leung, Y; Song, XX. Granular computing and dual Galois connection. Inf Sci; 2007; 177, pp. 5365-5377.
Mi, JS; Zhang, WX. An axiomatic characterization of a fuzzy generalization of rough sets. Inf Sci; 2004; 160,
Miller, FP; Vandome, AF; Mcbrewster, J. Alexandrov topology; 2010; Newton, Alphascript Publishing:
Morsi, NN; Yakout, MM. Axiomatics for fuzzy rough sets. Fuzzy Sets Syst; 1998; 100,
Pawlak, Z. Rough sets. Int J Comput Inform Sci; 1982; 11, pp. 341-356. [DOI: https://dx.doi.org/10.1007/BF01001956]
Pawlak, Z. Rough sets: theoretical aspects of reasoning about data; 1991; Boston, Kluwer Academic Publishers: [DOI: https://dx.doi.org/10.1007/978-94-011-3534-4]
Perfilieva, I et al. Harmati, IA et al. Q-fuzzy subtopology. Computational intelligence and mathematics for tackling complex problems 3, studies in computational intelligence; 2022; Berlin, Springer: pp. 1-6.
Perfilieva, I; Tiwari, SP; Singh, AP et al. Medina, J et al. Lattice-valued F-transforms as interior operators of -fuzzy pretopological spaces. Communications in computer and information science, IPMU; 2018; Berlin, Springer: pp. 163-174.
Perfilieva, I; Ramadan, AA; Elkordy, EH. Categories of -fuzzy Čech closure spaces and -fuzzy co-topological spaces. Mathematics; 2020; 8, 1274. [DOI: https://dx.doi.org/10.3390/math8081274]
Rosenthal, KI. Quantales and their applications; 1990; Burnt Mill, Longman House:
Shafer, G. A mathematical theory of evidence; 1976; Princeton, Princeton University Press: [DOI: https://dx.doi.org/10.1515/9780691214696]
Skowron, A. On the topology in information systems. Bull Polon Acad Sci Math; 1988; 36, pp. 477-480.
Skowron, A; Stepaniuk, J. Tolerance approximation spaces. Fundam Inform; 1996; 27,
Slowinski, R; Vanderpooten, D. A generalized definition of rough approximations based on similarity. IEEE Trans Knowl Data Eng; 2000; 12, pp. 331-336. [DOI: https://dx.doi.org/10.1109/69.842271]
Sostak, A; Uljane, I. Powerset operators induced by fuzzy relations as a basis for fuzzification of various mathematical structures. Int J Approx Reason; 2023; 155, pp. 17-33.
Steiner, AK. The lattice of topologies: structure and complementation. Trans Am Math Soc; 1966; 122, pp. 379-398.
Tiwari, SP; Srivastava, AK. Fuzzy rough sets, fuzzy preorders and fuzzy topoloiges. Fuzzy Sets Syst; 2013; 210, pp. 63-68. [DOI: https://dx.doi.org/10.1016/j.fss.2012.06.001]
Wang FY, Lin YT (1998) Linguistic dynamic systems and computing with words for modeling and simulation of complex systems. A tapestry of systems and Al-based theories and methodologies. In: Discrete event modeling and simulation technologies, pp 75–92
Wang, P. Computing with words; 2001; New York, Wiley:
Wiweger, A. On topological rough sets. Bull Polon Acad Sci Math; 1988; 37, pp. 51-62.
Wu, WZ; Leung, Y; Mi, JS. On characterizations of -fuzzy rough approximation operators. Fuzzy Sets Syst; 2005; 154,
Wu, WZ; Xu, YH; Shao, MW; Wang, GY. Axiomatic characterizations of -fuzzy rough approximation operators. Inf Sci; 2016; 334–335, pp. 17-43.
Yager RR, Filev D (1998) Operations for granular computing: mixing words and numbers. In: IEEE international conference on fuzzy systems, pp 123–128
Yao YY (1999) Granular computing using neighborhood systems. In: The 3rd online world conference on soft computing, London, pp 539–553
Yao, YY. Relational interpretations of neighborhood operators and rough set approximation operators. Inf Sci; 1998; 111, pp. 239-259.
Yao, YY. A partition model of granular computing. Trans Rough Sets; 2004; 1, pp. 232-253.
Yao, W; Han, S-E. A topological approach to rough set from a granule computing perspective. Inf Sci; 2023; 627, pp. 238-250. [DOI: https://dx.doi.org/10.1016/j.ins.2023.02.020]
Yao, W; Lu, L-X. Fuzzy Galois connections on fuzzy posets. Math Log Q; 2009; 55, pp. 105-112.
Yao, W; She, YH; Lu, LX. Metric-based -fuzzy rough sets: approximation operators and definable sets. Knowl Based Syst; 2019; 163, pp. 91-102. [DOI: https://dx.doi.org/10.1016/j.knosys.2018.08.023]
Yao, W; Zhang, G; Zhou, CJ. Real-valued hemimetric-based fuzzy rough sets and an application to contour extraction of digital surfaces. Fuzzy Sets Syst; 2023; 459, pp. 201-219.
Zadeh LA (1979) Fuzzy sets and information granularity. In: Gupta MM, Radade RK, Yager RR (eds) Advances in fuzzy set theory and applications. World Science Publishing, Amsterdam, pp 3–18
Zadeh, LA. Fuzzy logic = computing with words. IEEE Trans Fuzzy Syst; 1996; 4, pp. 103-111. [DOI: https://dx.doi.org/10.1109/91.493904]
Zadeh, LA. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst; 1997; 19, pp. 111-127.
Zhang L, Zhang B (2005) A quotient space approximation model of multi-resolution signal analysis. J Comput Sci Technol 20:90–94
Zheng Z, Hu H, Shi ZZ (2005) Tolerance relation based information granular space. In: IEEE international conference on granular computing, pp 367–372
Zhu, W. Relationship between generalized rough sets based on binary relation and covering. Inf Sci; 2009; 179, pp. 210-225.
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