Research article

Filter with its applications in fuzzy soft topological spaces

  • Received: 02 October 2020 Accepted: 04 December 2020 Published: 17 December 2020
  • MSC : 54A40, 54A20, 54A05

  • In this paper, we introduce the convergence of a fuzzy soft filter with the help of the Q-neighborhoods and study the relations between fuzzy soft nets and fuzzy soft filters. In addition, we use fuzzy soft filters to characterize some basic concepts of a fuzzy soft topological space, such as open sets, closure, T2 separation and continuity.

    Citation: Rui Gao, Jianrong Wu. Filter with its applications in fuzzy soft topological spaces[J]. AIMS Mathematics, 2021, 6(3): 2359-2368. doi: 10.3934/math.2021143

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  • In this paper, we introduce the convergence of a fuzzy soft filter with the help of the Q-neighborhoods and study the relations between fuzzy soft nets and fuzzy soft filters. In addition, we use fuzzy soft filters to characterize some basic concepts of a fuzzy soft topological space, such as open sets, closure, T2 separation and continuity.



    In 2001, Maji et al. [14] combined fuzzy sets [26] with soft sets [15] and proposed the concept of fuzzy soft sets. After that, the fuzzy soft set was applied to group theory, decision making, medical diagnosis and other fields (see [1,3,7,8,12,17,23,24,25,27]). Meanwhile, the theory of fuzzy soft set has been developed rapidly. Especially, the research on fuzzy soft topology has made a lot of achievements (see [2,6,9,10,11,13,16,18,19,20,21]).

    Noting the contribution of the point approach in fuzzy topology, Roy and Samanta [18] defined a fuzzy soft point in a fuzzy soft topological space. In 2018, Ibedou and Abbas [10] redefined this concept. Recently, Gao and Wu [8] studied the properties of fuzzy soft point introduced in [10] deeply, and pointed that the fuzzy soft point given in [10] was more effective than that given in [18]. They also gave the definitions of a fuzzy soft net consisting of fuzzy soft points and its convergence. On these bases, they characterized the continuity of fuzzy soft mappings by the net approach. In 2014, Cetkin and Aygun [4] proposed the concept of fuzzy soft filters. Besides, Izzettin Demir et al. [5] investigated the convergence theory of fuzzy soft filters by using the technique of neighborhoods. Moreover, they used the fuzzy soft filter convergence to characterize closure, continuity, product space and T2 separation. Finally, they defined the notion of a fuzzy soft filter base and a fuzzy soft ultrafilter and obtained a few results analogous to the ones that held for fuzzy ultrafilters.

    It is well known that Q-neighborhoods method has more merits than neighborhoods method. In Section 3, this paper redefines the concept of fuzzy soft filters convergence with the help of the Q-neighborhoods. In Section 4, the fuzzy soft filters are used to characterize some basic concepts of a fuzzy soft topological space, such as open sets, closure, T2 separation and continuity. Finally, a brief conclusion is given in Section 5.

    Throughout this paper, U refers to an initial universe, and E is the set of all parameters for U. In this case, U is also denoted by (U, E). IU is the set of all fuzzy subsets over U, where I = [0, 1]. The elements 0,1IU respectively refer to the functions 0(x)=0 and 1(x)=1 for all xU. For an element AIU, if there exists an xU such that A(x)=λ>0 and A(y)=0, yU{x}, then A is called a fuzzy point over U and is denoted by xλ, meanwhile, x and λ are called the support and height of xλ, respectively. The set of all fuzzy points over U is denoted by FP(U).

    The definitions in this section are sourced from the existing literature [8,10,18,19].

    Definition 2.1. Let AE. A mapping FA:EIU, is called a fuzzy soft set over (U,E), where FA(e)=ˉ0 if eEA and FA(e)ˉ0 if eA.

    The set of all fuzzy soft sets over (U, E) is denoted by FS(U, E).

    The fuzzy soft set FφFS(U,E) is called the null fuzzy soft set and is denoted by ˜Φ. Here, Fϕ(e)=ˉ0 for every eE.

    For FEFS(U,E), if FE(e)=ˉ1 for all eE, then FE is called the absolute fuzzy soft set and is denoted by ˜E.

    Let FA,FBFS(U,E). If FA(e)FB(e) for all eE, then FA is said to be a fuzzy soft subset of FB and is denoted by FA˜FB or FB˜FA. If FA˜FB and FB˜FA, then FA and FB are said to be equivalent, denoted by FA=FB.

    Remark 2.1. If FA˜FB, then AB.

    Definition 2.2. Let FA,FBFS(U,E).

    (1) The complement of FA, denoted by FcA, is then defined as

    FcA(e)={ˉ1FA(e)foreA,ˉ1,otherwise.

    (2) The union of FA and FB is also a fuzzy soft set FC defined by FC(e)=FA(e)FB(e) for all eE, where C=AB, and is denoted by FC=FA˜FB.

    (3) The intersection of FA and FB is also a fuzzy soft set FC defined by FC(e)=FA(e)FB(e) for all eE, where C=AB, and is denoted by FC=FA˜FB.

    Similarly, the union (intersection) of a family of fuzzy soft sets may be defined as {FCα:αΛ} and denoted by ˜αΛFCα(˜αΛFCα), where Λ is an arbitrary index set.

    Remark 2.2. It is therefore clear that:

    (1) ˜Φc=˜E,˜Ec=˜Φ;

    (2) (˜αΛFAα)c=˜αΛFcAα, (˜αΛFAα)c=˜αΛFcAα.

    Definition 2.3. A fuzzy soft topology τ over (U,E) is a family of fuzzy soft sets over (U,E) satisfying the following properties;

    (1) ˜Φ,˜Eτ;

    (2) if FA,FBτ, then FA˜FBτ;

    (3) if FAατ for all αΛ (an index set), then ˜αΛFAατ.

    If τ is a fuzzy soft topology over (U,E), the triple (U,E,τ) is said to be a fuzzy soft topological space. Each element of τ is called an open set. If FcA is an open set, then FA is called a closed set.

    Definition 2.4. Let (U,E,τ) be a fuzzy soft topological space, FAFS(U,E).

    (1) The intersection of all closed sets FB˜FA is called the closure of FA and is denoted by ¯FA.

    (2) The union of all open subsets of FA over (U,E,τ) is called the interior of FA and is denoted by intFA.

    Definition 2.5. A mapping ξ:EIU is called a fuzzy soft point over (U,E) if there is an eE such that ξ(e)=xλFP(U), and ξ(a)=ˉ0 when aE{e}.

    In this case, ξ is also denoted by Pxλe, and e is called its parameter support. The set of all fuzzy soft points over (U,E) is denoted by FSP(U,E).

    A fuzzy soft point is called a point if no confusion arises.

    For Pxλe, PyβfFSP(U,E), it is said that Pxλe is equal to Pyβf, denoted by Pxλe = Pyβf, if and only if x = y, λ = β and e = f.

    Definition 2.6. A point Pxλe is said to be quasi-coincident with FAFS(U,E), which is denoted by Pxλe˜FA, if λ+FA(e)(x)>1 for some xU.

    Definition 2.7. A fuzzy soft set FA is said to be quasi-coincident with FB, which is denoted by FAqFB, if FA(e)(x)+FB(e)(x)>1 for some xU and eAB.

    On the contrary, a fuzzy soft set FA is said to be not quasi-coincident with FB, which is denoted by FAˉqFB, if FA(e)(x)+FB(e)(x)1 for all xU and eAB.

    Definition 2.8. Let ξFSP(U,E) and FA,FBFS(U,E) on a fuzzy soft topological space (U,E,τ).

    (1) FA is said to be a neighborhood of ξ if there exists FBτ such that ξFB˜FA.

    (2) FA is called a Q-neighborhood of ξ if there exists FBτ such that ξ˜FB˜FA.

    The set of all Q-neighborhoods of ξ is denoted by A(ξ).

    Remark 2.3. It is clear that A(ξ) is a directed set with the partial order "˜".

    Definition 2.9. Let Δ be a directed set with the partial order "". If S(δ)FSP(U,E) for any δΔ, then {S(δ),δΔ} is said to be a fuzzy soft net over (U,E), and is denoted by S for simplicity.

    In particular, if {S(δ),δΔ} is a fuzzy soft net over (U,E), and there exists an FAFS(U,E) such that S(δ)FA for any δΔ, then {S(δ),δΔ} is said to be a fuzzy soft net in FA.

    A fuzzy soft net is called a net for simplicity if no confusion arises.

    Definition 2.10. Let FAFS(U,E) and S={S(δ),δΔ} be a net over (U,E). If there exists δ0Δ such that S(δ)˜FA whenever δ0δ, then S is said to be eventually quasi-coincident with FA. If for each δΔ there exists δ0Δ with δδ0 such that S(δ0)˜FA, then S is said to be frequently quasi-coincident with FA.

    Definition 2.11. A net {S(δ),δΔ} over (U,E,τ) is said to be convergent to a point ξ if S is eventually quasi-coincident with each Q-neighborhood of ξ. In this case, ξ is called the limit of S and is denoted by limS(δ).

    In this section, we introduce the convergence of a fuzzy soft filter by using the Q-neighborhoods and study the relations between fuzzy soft nets and fuzzy soft filters.

    Definition 3.1 [4]. A fuzzy soft filter Φ on (U,E,τ) is a nonempty collection of subsets of FS(U,E) with the following properties:

    (FSF1) ˜ΦF,

    (FSF2) If FA,FBF, then FA˜FBF,

    (FSF3) If FAF and FA˜FB, then FBF.

    If F1 and F2 are two fuzzy soft filters on (U,E,τ), we say that F1 is finer than F2 (or F2 is coarser than F1) if and only if F1F2.

    The set of all fuzzy soft filters over (U,E) is denoted by FSF(U,E).

    Example 3.1. Let ξFSP(U,E), F={FAFS(U,E)|ξ˜FA} be a fuzzy soft filter. A(ξ) is also a fuzzy soft filter and called the Q-neighborhood filter of ξ.

    Definition 3.2. Let F be a fuzzy soft filter in a fuzzy soft topological space (U,E,τ). F is said to be convergent to the fuzzy soft point ξ, denoted by , if A(ξ)F.

    Now, we investigate the relations between fuzzy soft nets and fuzzy soft filters.

    First, we show that a fuzzy soft filter may generate a fuzzy soft net. In fact, let F={FAi,iI}FSF(U,E) and

    S(i)˜FAi for all iI. (3.1)

    The index set I forms a directed set under the relation "", where ii if and only if FAi˜FAi for any i,iI. Then SF={S(i),iI} is a fuzzy soft net.

    Next, we show that a fuzzy soft net may generate a fuzzy soft filter. Suppose thatS={S(δ),δΔ} is a fuzzy soft net in a fuzzy soft topological space (U,E,τ). Let

    Fs={FAFS(U,E),S is eventually quasi-coincident with FA}. (3.2)

    It is easy to see that FSFSF(U,E).

    Definition 3.3. (1) Let F={FAδ,δΔ}FSF(U,E), and let S(i) be defined as (3.1). Then SF={S(i),iI} is said to be the fuzzy soft net generated by F.

    (2) Let S={S(δ),δΔ} be a fuzzy soft net in a fuzzy soft topological space (U,E,τ), and FS be defined as (3.2). Then FS is said to be the fuzzy soft filter generated by S.

    In the rest of this paper, SF and FS represent the fuzzy soft net generated by F and the fuzzy soft filter generated by S respectively.

    Theorem 3.1. Let (U,E,τ) be a fuzzy soft topological space and ξFSP(U,E). F={FAi,iI}FSF(U,E), S={S(δ),δΔ} is a fuzzy soft net on (U,E). Then:

    (1) F converges to ξ if and only if SF converges to ξ.

    (2) S converges to ξ if and only if FS converges to ξ.

    Proof. (1) (Necessity) Since F converges to ξ, then A(ξ)F. For each FAA(ξ), there exists iI such that FAi=FA. Therefore, FAi˜FAi when iiI. So, S(i)˜FA when iiI. Thus, SF converges to ξ.

    (Sufficiency) Suppose F does not converge to ξ. Then, there exists an FAA(ξ) such that FAF. Therefore, FAi˜FA for each iI. That is, there exist xiU and eiE such that FAi(ei)(xi)>FA(ei)(xi) for each iI. Take λi(0,1) such that FAi(ei)(xi)>1λi>FA(ei)(xi). Let S(i)=P(xi)λiei, then S(i)˜FAi and S(i)˜FA. Therefore, the fuzzy soft net SF={S(i),iI} does not converge to ξ, which is a contradiction. Thus, F converges to ξ.

    (2) (Necessity) Since S converges to ξ, then for each FAA(ξ), S is eventually quasi-coincident with FA. That is FAFS. Therefore, FS converges to ξ.

    (Sufficiency) If FS converges to ξ, then A(ξ)F. That is, for each FAA(ξ), S is eventually quasi-coincident with FA. Therefore, S converges to ξ.

    In this section, we use fuzzy soft filters to describe open sets, closure, T2 separation and continuity in fuzzy soft topological spaces. First, we give a lemma. Its proof is easy.

    Lemma 4.1. Let (U,E,τ) be a fuzzy soft topological space. If for any ξ˜FAFS(U,E), there exists an open set FBA(ξ) such that FB˜FA, FC=˜{FB|ξ˜FA}, then FC=FA.

    Theorem 4.1. Let (U,E,τ) be a fuzzy soft topological space. FAFS(U,E) is open if and only if FAF for any fuzzy soft point ξ˜FA and FFSF(U,E) with .

    Proof. (Necessity) Since FA is open and ξ˜FA, then FAA(ξ). So FAF follows from .

    (Sufficiency) Let ξ˜FA arbitrarily. Since limA(ξ)=ξ, then FAA(ξ). That is, there exists an open set FBA(ξ), such that FB˜FA. From Lemma 4.1, FA = ˜{FB|ξ˜FA} is open.

    Remark 4.1. In [8], it is proved that Pxλe˜FA if and only if PxλeFcA. Additionally, if Pxλe˜FA, then there exists 0<μ<λ such that Px1μeFA.

    Theorem 4.2. Let (U,E,τ) be a fuzzy soft topological space. A fuzzy soft point ξ¯FA if and only if there exists FFSF(U,E) such that and FBqFA for any FBF.

    Proof. (Necessity) Let ξ¯FA, F=A(ξ), then . For any FBA(ξ), we suppose that FB is open without loss of generality. To complete the proof, it is sufficient to show that FBqFA. In fact, if FBˉqFA, then FA(d)(y)+FB(d)(y)1 for any yU and dE. Hence, FA˜FcB. Noting that FcB is closed, one gets ¯FA˜FcB. Therefore, ξFcB. It follows from Remark 4.1 that ξ˜FB, which conflicts with FBA(ξ). Thus, FBqFA.

    (Sufficiency) Suppose that ξ¯FA, then ξ˜¯FAc from Remark 4.1. Therefore, ¯FAcA(ξ). Since , then A(ξ)F. Therefore, ¯FAcF, and hence ¯FAcqFA. That is, there exist yU and dE such that ¯FAc(d)(y)+FA(d)(y)>1. Equivalently, ¯FA(d)(y)<FA(d)(y), which is a contradiction. Thus, ξ¯FA.

    Definition 4.1. Let (U,E,τ) be a fuzzy soft topological space. If for any two different points ξ and ζ, there exist FAA(ξ) and FBA(ζ) such that FA˜FB=˜Φ, then (U,E,τ) is said to be T2 separated.

    Definition 4.2 [5]. A collection B of subsets of FS(U,E) is called a base for a fuzzy soft filter on (U,E,τ) if the following two conditions are satisfied:

    (B1) BΦ and ˜ΦB,

    (B2) If FA, FBB, then there is a FCB such that FC˜FA˜FB.

    One readily sees that if B is a base for a fuzzy soft filter on (U,E,τ), the collection

    FB={FAFS(U,E): there exists a FCB such that FC˜FA}

    is a fuzzy soft filter on (U,E,τ). We say that the fuzzy soft filter FB is generated by B.

    Theorem 4.3. A fuzzy soft topological space (U,E,τ) is T2 separated if and only if for any FFSF(U,E) does not converge to two different points at the same time.

    Proof. (Necessity) Let FFSF(U,E) with =ξFSP(U,E). For any fuzzy soft point ζξ, since (U,E,τ) be T2 separated, there exist FAA(ξ) and FBA(ζ) such that FA˜FB=˜Φ. From FAF, one knows that FBF. Therefore, F does not converge to ζ.

    (Sufficiency) Suppose (U,E,τ) is not T2 separated. Then there exist two different points ξ and ζ such that FA˜FB˜Φ for any FAA(ξ) and FBA(ζ). It is easy to see that B = {FA˜FB|FAA(ξ), FBA(ζ)} is a fuzzy soft filter base, and the fuzzy soft filter generated by B converges to ξ and ζ at the same time, which contradicts with the condition. Thus (U,E,τ) is T2 separated.

    The following definition originates from [3].

    Definition 4.3. Let φ:U1U2 and ψ:E1E2 be two functions. Then, the pair (φ,ψ) is called a fuzzy soft mapping from (U1,E1) to (U2,E2).

    (1) Let FAFS(U1,E1). Then, the image of FA under (φ,ψ) is the fuzzy soft set over (U2,E2) defined by (φ,ψ)(FA), where

    (φ,ψ)(FA)(k)(y)={φ(x)=yψ(e)=kFA(e)(x),ifφ1(y)ϕˉ0,otherwise,kψ(E1),yU2.

    (2) Let FBFS(U2,E2). Then, the pre-image of FB under (φ,ψ) is the fuzzy soft set over (U1,E1) defined by (φ,ψ)1(FB), where

    (φ,ψ)1(FB)(e)(x)=FB(ψ(e))(φ(x)), eψ1(E2), xU1

    If both φ and ψ are injective (surjective), then the fuzzy soft mapping (φ,ψ) is said to be injective (surjective).

    The composition of two fuzzy soft mappings (φ,ψ) from (U1,E1) to (U2,E2) and (φ,ψ) from (U2,E2) to (U3,E3) is defined as (φφ,ψψ) from (U1,E1) to (U3,E3).

    Definition 4.4. Let (U1,E1,τ1) and (U2,E2,τ2) be two fuzzy soft topological spaces, ξFSP(U1,E1). A fuzzy soft mapping (φ,ψ): (U1,E1,τ1)(U2,E2,τ2) is said to be fuzzy soft continuous at ξ if for any FBA((φ,ψ)(ξ)), there exists FAA(ξ), such that (φ,ψ)(FA)˜FB.

    A fuzzy soft mapping (φ,ψ): (U1,E1,τ1)(U2,E2,τ2) is said to be fuzzy soft continuous if (φ,ψ) is fuzzy soft continuous at each fuzzy soft point of (U1,E1,τ1).

    Remark 4.2. Theorem 6 in [8] implies that the fuzzy soft continuity of a fuzzy soft mapping in this paper is equivalent to that in [22].

    Lemma 4.2 [22]. Let (U1,E1,τ1) and (U2,E2,τ2) be two fuzzy soft topological spaces and (φ,ψ): (U1,E1,τ1)(U2,E2,τ2) is a fuzzy soft mapping. Then (φ,ψ) is fuzzy soft continuous if and only if (φ,ψ)(¯FA)˜¯(φ,ψ)(FA), FAFS(U1,E1).

    Lemma 4.3. Let (U1,E1,τ1) and (U2,E2,τ2) be two fuzzy soft topological spaces and (φ,ψ): (U1,E1,τ1)(U2,E2,τ2) is surjective, FFSF(U1,E1), then (φ,ψ)(F)FSF(U2,E2).

    Proof. (FSF1) and (FSF2) are obvious.

    (FSF3) Let FAF and (φ,ψ)(FA)˜FBFS(U2,E2). Since FA˜(φ,ψ)1(φ,ψ)(FA)˜(φ,ψ)1(FB), then FA1=(φ,ψ)1(FB)F. Noting that (φ,ψ) is surjective, one gets that FB=(φ,ψ)(φ,ψ)1(FB)=(φ,ψ)(FA1)(φ,ψ)(F).

    Therefore, (φ,ψ)(F)FSF(U2,E2).

    Theorem 4.4. Let (U1,E1,τ1) and (U2,E2,τ2) be two fuzzy soft topological spaces, and (φ,ψ): (U1,E1,τ1)(U2,E2,τ2) be surjective. Then, (φ,ψ) is fuzzy soft continuous if and only if lim(φ,ψ)(F)(φ,ψ)(ξ) for any FFSF(U1,E1) with FSP(U1,E1).

    Proof. (Necessity) Since (φ,ψ) is fuzzy soft continuous, by Definition 4.4, for any FBA((φ,ψ)(ξ)), there exists FAA(ξ), such that (φ,ψ)(FA)˜FB. Let FFSF(U1,E1) with . Then FAF, and (φ,ψ)(FA)(φ,ψ)(F). Therefore, FB(φ,ψ)(F). So .

    (Sufficiency) To complete the proof, we shall show that (φ,ψ)(¯FA)˜¯(φ,ψ)(FA) for any FAFS(U1,E1). Take ζ(φ,ψ)(¯FA) arbitrarily. Then there is ξ¯FA such that ζ=(φ,ψ)(ξ). By Theorem 4.2, there exists FFSF(U1,E1) such that and FBqFA for any FBF. From Lemma 4.3, (φ,ψ)(F)FSF(U2,E2), and .

    For any FC(φ,ψ)(F), there is FBF such that FC=(φ,ψ)(FB). Owing to FBqFA and Theorem 5 in [8], we have ((φ,ψ)(FB))q((φ,ψ)(FA)). That is, FCq((φ,ψ)(FA)). It follows from Theorem 4.2 that ζ¯(φ,ψ)(FA). Recalling the arbitrariness of ζ(φ,ψ)(¯FA), we have (φ,ψ)(¯FA)˜¯(φ,ψ)(FA).

    In this paper, the convergence of a fuzzy soft filter is redefined by the Q-neighborhoods, some important properties of fuzzy soft topological spaces are characterized by the fuzzy soft filter. The obtained results demonstrate that the methods proposed in this paper are very useful and will provide powerful research tools for further research in this field.

    This work is supported by the National Natural Science Foundation of China (11971343) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX19_2015).

    The authors declare that there is no conflict of interest in this paper.



    [1] S. Alkhazaleh, A. R. Salleh, N. Hassan, Fuzzy parameterized interval-valued fuzzy soft set, Applied Mathematical Sciences, 5 (2011), 3335–3346.
    [2] A. Aygünoğlu, E. Aydoğdu, H. Aygün, Fuzzy soft metric and fuzzifying soft topology induced by fuzzy soft metric, Filomat, 33 (2019), 645–653. doi: 10.2298/FIL1902645A
    [3] A. Aygünoǧlu, H. Aygün, Introduction to fuzzy soft groups, Comput. Math. Appl., 58 (2009), 1279–1286. doi: 10.1016/j.camwa.2009.07.047
    [4] V. Cetkin, H. Aygun, On convergence of fuzzy soft filters, 3rd International Eurasian Conference on Mathematical Sciences and Applications, Vienna, Austria, August, (2014), 25–28.
    [5] I. Demir, O. B. Ozbakır, I. Yıldız, Fuzzy soft ultrafilters and convergence properties of fuzzy soft filters, Journal of New Results in Science, 8 (2015), 92–107.
    [6] S. El-Shiekh, S. El-Sayed, γ-Operation & Decomposition of Some Forms of Fuzzy Soft Mappings on Fuzzy Soft Ideal Topological Spaces, Filomat, 34 (2020), 187-196.
    [7] F. Feng, Y. M. Li, V. Leoreanu-Fotea, Application of level soft sets in decision making based on interval-valued fuzzy soft sets, Comput. Math. Appl., 60 (2010), 1756–1767.
    [8] R. Gao, J. R. Wu, A net with applications for continuity in a fuzzy soft topological space, Math. Probl. Eng., 2020 (2020), 9098410.
    [9] C. Gunduz (Aras), S. Bayramov, Some results on fuzzy soft topological spaces, Math. Probl. Eng., 2013 (2013), 835308.
    [10] I. Ibedou, S. E. Abbas, Fuzzy Soft Filter Convergence, Filomat, 32 (2018), 3325–3336. doi: 10.2298/FIL1809325I
    [11] A. Kandil, O. A. El-Tantawy, S. A. El-Sheikh, S. S. S. El-Sayed, Fuzzy soft connected sets in fuzzy soft topological spaces Ⅱ, Journal of the Egyptian Mathematical Society, 25 (2017), 171–177.
    [12] Z. Kong, L. Gao, L. Wang, Comment on "A fuzzy soft set theoretic approach to decision making problems", J. Comput. Appl. Math., 223 (2009), 540–542.
    [13] J. Mahanta, P. K. Das, Results on fuzzy soft topological spaces, arXiv: 1203.0634v1, 2012.
    [14] P. K. Maji, R. Biswas, A. R. Roy, Fuzzy soft sets, J. Fuzzy Math., 9 (2001), 589–602.
    [15] D. Molodtsov, Soft set theory-first results, Comput. Math. Appl., 37 (1999), 19–31.
    [16] J. S. Ping, T. Wu, C. Z. Yang, Sum spaces in fuzzy soft topological spaces, Fuzzy Systems and Mathematics, 28 (2014), 69–73.
    [17] A. R. Roy, P. K. Maji, A fuzzy soft set theoretic approach to decision making problems, J. Comput. Appl. Math., 203 (2007), 412–418. doi: 10.1016/j.cam.2006.04.008
    [18] S. Roy, T. K. Samanta, An introduction to open and closed sets on fuzzy soft topological spaces, Ann. Fuzzy Math. Inform., 6 (2013), 425–431.
    [19] S. Roy, T. K. Samanta, A note on fuzzy soft topological spaces, Ann. Fuzzy Math. Inform., 3 (2012), 305–311.
    [20] T. Simsekler, S. Yuksel, Fuzzy soft topological spaces, Ann. Fuzzy Math. Inform., 5 (2013), 87–96.
    [21] B. Tanay, M. B. Kandemir, Topological structure of fuzzy soft sets, Comput. Math. Appl., 61 (2011), 2952–2957. doi: 10.1016/j.camwa.2011.03.056
    [22] B. P. Varol, H. Aygün, Fuzzy soft topology, Hacet. J. Math. Stat., 41 (2012), 407–419.
    [23] J. W. Wang, Y. Hu, F. Y. Xiao, X. Y. Deng, Y. Deng, A novel method to use fuzzy soft sets in decision making based on ambiguity measure and dempster-shafer theory of evidence: an application in medical diagnosis, Artif. Intell. Med., 69 (2016), 1–11. doi: 10.1016/j.artmed.2016.04.004
    [24] Z. Xiao, K. Gong, Y. Zou, A combined forecasting approach based on fuzzy soft sets, J. Comput. Appl. Math., 228 (2009), 326–333. doi: 10.1016/j.cam.2008.09.033
    [25] N. X. Xie, G. Q. Wen, Z. W. Li, A method for fuzzy soft sets in decision making based on grey relational analysis and d-s theory of evidence: application to medical diagnosis, Comput. Math. Method. M., 2014 (2014), 581316.
    [26] L. A. Zadeh, Fuzzy sets, Inf. Control, 8 (1965), 338–353.
    [27] Z. M. Zhang, S. H. Zhang, A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets, Appl. Math. Model., 37 (2013), 4948–4971. doi: 10.1016/j.apm.2012.10.006
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