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Optimistic multigranulation roughness of a fuzzy set based on soft binary relations over dual universes and its application

  • Received: 06 December 2022 Revised: 05 February 2023 Accepted: 06 February 2023 Published: 28 February 2023
  • MSC : 03B52, 68T27

  • A fascinating extension of Pawlak rough set theory to handle uncertainty is multigranulation roughness, which has been researched by several researchers over dual universes. In light of this, we proposed a novel optimistic multigranulation roughness of a fuzzy set based on soft binary relations over dual universes and established two types of approximations of a fuzzy set with respect to forsets and aftersets of the finite number of soft binary relations in this article. We obtain two sets of fuzzy soft sets in this way, referred to as the lower approximation and upper approximation with respect to the aftersets and the foresets, respectively. Next, we look into some of the lower and higher approximations of the newly multigranulation rough set model's algebraic properties. Both the roughness and accuracy measurements were defined. In order to show our suggested model, we first develop a decision-making algorithm. Then, we give an example from a variety of applications.

    Citation: Jamalud Din, Muhammad Shabir, Nasser Aedh Alreshidi, Elsayed Tag-eldin. Optimistic multigranulation roughness of a fuzzy set based on soft binary relations over dual universes and its application[J]. AIMS Mathematics, 2023, 8(5): 10303-10328. doi: 10.3934/math.2023522

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  • A fascinating extension of Pawlak rough set theory to handle uncertainty is multigranulation roughness, which has been researched by several researchers over dual universes. In light of this, we proposed a novel optimistic multigranulation roughness of a fuzzy set based on soft binary relations over dual universes and established two types of approximations of a fuzzy set with respect to forsets and aftersets of the finite number of soft binary relations in this article. We obtain two sets of fuzzy soft sets in this way, referred to as the lower approximation and upper approximation with respect to the aftersets and the foresets, respectively. Next, we look into some of the lower and higher approximations of the newly multigranulation rough set model's algebraic properties. Both the roughness and accuracy measurements were defined. In order to show our suggested model, we first develop a decision-making algorithm. Then, we give an example from a variety of applications.



    In this paper, let J=(0II0) and N=(I00I) with I being the identity matrix on Rn. Denote by Ls(R2n) the space of all symmetric matrices in R2n and Sp(2n) the symplectic group of 2n×2n matrices.

    We call BC(S1,Ls(R2n)) satisfies condition (BS1) if B(t)=NB(t)N for all tR and B is 12-periodic, where S1=R/Z.

    Let HC1(R×R2n,R) and H denote the gradient of H with respect to the last 2n variables. We assume H satisfies the following conditions:

    (H1)H(t,x)=B0(t)x+o(|x|) as |x|0 uniformly in t,

    (H2)H(t,x)=Bx+o(|x|) as |x| uniformly in t,

    (H3) H(t,Nx)=H(t,x)=H(t,x)=H(t+12,x), (t,x)(R×R2n).

    In [1] of 2008, the second author of this paper considered the multiplicity of 1-periodic brake orbits of asymptotically linear symmetric reversible Hamiltonian systems. Since condition (H3) holds, it is natural to consider 1-periodic solution of the asymptotically linear Hamiltonian systems

    ˙x=JH(t,x), (1.1)
    x(t+1)=x(t),x(12+t)=Nx(12t),x(12+t)=x(t),tR. (1.2)

    We call the above 1-periodic solutions symmetric brake orbits. If H(t,Nx)=H(t,x) for all (t,x)(R×R2n), we say H is reversible.

    Note that conditions (H1)-(H3) yield that both B0 and B belong to C(S1,Ls(R2n)) and satisfy the above (BS1) condition.

    In 1948, Seifert firstly studied brake orbits in Hamiltonian system in [2]. For the existences and multiple existence results and more details on brake orbits one can refer the paper [3,4] and the references therein. In [1] the second author of this paper obtained multiple existence of brake orbits of the asymptotically linear Hamiltonian systems (1.1)-(1.2) under certain conditions. In this paper we will study multiplicity of symmetric brake orbits of Hamiltonian systems (1.1)-(1.2).

    In [5], the difference between B0 and B, i.e., the different behaviors of H at zero and infinity plays an important role in the study of 1-periodic solutions of (1.1). For this reason we also define the relative Morse index to measure the "true" difference between B0 and B under symmetric brake orbit boundary value. We shall study the relation between the relative Morse index and the Maslov-type index iL01 and νL01 defined below. As application, we obtain a multiplicity of symmetric brake orbits (1.1)-(1.2).

    For any symplectic path γ in Sp(2n), the Maslov-type index for symmetric brake orbits boundary values of γ is defined in [6] to be a pair of integers (iL0(γ),νL0(γ))Z×{0,1,2,...,n}.

    For any continuous path B in Ls(R2n) satisfying condition (BS1), as in [6], we define

    (iL01(B),νL01(B))=(iL01(γB,[0,14]),νL01(γB(14))), (1.3)

    where the symplectic path γB is the fundamental solution of the following linear Hamiltonian system

    ddtγB(t)=JB(t)γB(t)andγB(0)=I2n. (1.4)

    We will briefly introduce such Malov-type index theory in Section 2.

    In order to consider the multiplicity of symmetric brake orbits, define

    ˜E={xW1/2,2(S1,R2n)x(t)=Nx(t),x(t+12)=x(t)a.e.tR}. (1.5)

    Equip ˜E with the usual W1/2,2 norm. Then ˜E is a Hilbert space with the associated inner product ,. We define two self adjoint liner operators ˜A, ˜B from ˜E to ˜E by

    ˜Ax,y=10(J˙x,y)dt,˜Bx,y=10(B(t)x,y)dt,x,y˜E, (1.6)

    where B is a continuous path in Ls(R2n) satisfying condition (BS1).

    As in [5], we denote by M+(), M(), and M0() the positive definite, negative definite, and null subspaces of the self adjoint linear operator defining it respectively.

    Definition 1.1. Let B1 and B2 be continuous paths in Ls(R2n) satisfying condition (BS1). We define the relative Morse index of ˜B1 and ˜B2 by

    I(˜B1,˜B2)=dim(M+(˜A˜B1)M(˜A˜B2))dim((M(˜A˜B1)M0(˜A˜B1))(M+(˜A˜B2)M0˜A˜B2)), (1.7)

    where we also denote by ˜B1 and ˜B2 the operators defined by (1.6) respectively. We call I(˜B1,˜B2) the relative Morse index, following [5]. By Theorem 1.2 below this relative More index is well defined. Note that such definition of relative Morse index is different from those defined in [7], [8], and [9] etc, which is difference of Morse index or spectral flow or definition from Garlerkin approximation, the definition if dimker(AB1).

    Using the iteration theory of Maslov-type index theory and result in [1], we obtain the relation between the Maslov-type index (iL01, νL01) and the relative Morse index as the following

    Theorem 1.1. Let B1 and B2 be be continuous paths in Ls(R2n) satisfying condition (BS1). We have

    I(˜B1,˜B2)=iL01(B2)iL01(B1)νL01(B1). (1.8)

    As application, we obtain the main result of this paper.

    Theorem 1.2. Suppose that H satisfies (H1), (H2), (H3), and νL01(B0)=νL01(B)=0. Then (1.1)-(1.2) has at least |iL01(B1)iL01(B)| pairs of nontrivial 1-periodic symmetric brake orbits.

    Organization: In Section 2, we will briefly introduce the Maslov-type index and its iteration theory for symplectic path under brake orbit boundary value. Based on this index theory we give he proof of Theorem 1.1. As application, in Section 3, we give he proof of Theorem 1.2.

    Throughout this paper, let N, Z, R, C and U denote the set of natural integers, integers, rational numbers, real numbers, complex numbers and the unit circle in C, respectively.

    In this section we will prove Theorem 1.1. We first simply recall the Maslov-type index and its iteration theory for brake orbits.

    As we know, in 1984, Conley and Zehnder in their celebrated paper [10] introduced an index theory for the non-degenerate symplectic paths in the real symplectic matrix group Sp(2n) for n2. Since then, there are tremendous works about this kind of index theory developed or generalized in various directions. In 2006, combined with the Maslov index formulated in [11], Long, Zhang and Zhu developed an index theory called μ-index in [12] and obtained an result on the existence of multiple brake orbits. The difference of the Maslove-type μ-index in [12] and the Maslov-type L-index in that paper is constant n (half of the dimension). In [13], Liu and Zhang established the Bott-type iteration formulas and some precise iteration formula of the L-index theory and proved the multiplicity of brake orbits on every C2 compact convex symmetric hypersurface in R2n.

    Set

    PT(2n)={γC([0,T],Sp(2n))γ(0)=I2n},

    where we omit T from the notation of PT if [0,T] is replaced by [0,+). Let J be the standard almost complex in (R2n,ω0) and J is a compatible with ω0, i.e.,

    ω0(x,y)=Jxy,ω0(Jx,Jy)=ω0(x,y)andω0(x,Jx)>0forx0.

    A n-dimensional subspace ΛR2n is called a Lagrangian subspace if ω0(x,y)=0, for any x,yΛ. Let F=R2nR2n be equipped with symplectic form (ω0)ω0. Then J=(J)J is an almost complex structure on F and J is compatible with (ω0)ω0. Denote by Lag(F) the set of Lagrangian subspaces of F. Then for any MSp(2n), its graph

    Gr(M)={(xMx)|xR2n}Lag(F).

    Denote by L0={0}×Rn and L1=Rn×{0} the two fixed Lagrangian subspaces of R2n and let

    V0=L0×L0,  V1=L1×L1,Gr(M)|Vj={(xMy)|x,yLj}. (2.1)

    Then both Vj,Gr(M)|VjLag(F) for MSp(2n) and j=0,1.

    Denote by μCLMF(V,W,[a,b]) the Maslov-type index for (ordered) pair of paths of Lagrangian subspaces (V,W) in F on [a,b], which is defined by Cappel, Lee and Miller in [11].

    Definition 2.1. (cf. [6,12,13]) For γPτ(2n), define

    iLjω(γ)={μCLMF(Gr(eθJ)|Vj,Gr(γ(t)),t[0,τ]),ω=e1θU{1},  μCLMF(Vj,Gr(γ(t)),t[0,τ])n,ω=1, (2.2)
    νLjω(γ)=νLjω(γ(τ))=dimC(γ(τ)LjeθJLj),  ω=e1θU. (2.3)

    For j=0,1, we define (iLj(γ),νLj(γ))=(iLjω(γ),νLjω(γ)) if ω=1. Note that, for any continuous path ΨPτ, the following Maslov-type indices of Ψ is defined by (cf [1,12])

    μ1(Ψ,[a,b])=μCLMF(V0,Gr(Ψ),[0,τ]),ν(Ψ,[0,τ])=dimΨ(τ)L0L0. (2.4)

    When there is no confusion we will omit the intervals in the above definitions. Hence we have

    iL0(γ)=μ1(γ)n,νL0(γ)=ν1(γ), (2.5)

    For BC(ST,Ls(R2n)), the fundamental solution γB of the linear Hamiltonian system

    {˙γ(t)=JB(t)γ(t),γ(0)=I2n. (2.6)

    satisfies γBPT(2n), and is called the associated symplectic path of B. For ωU, we define the Maslov-type indices of B via the restriction γB|[0,T/2]PT/2(2n) :

    (iLjω(B,T2),νLjω(B,T2)):=(iLjω(γB|[0,T/2]),νLjω(γB(T2))).

    In 1956, Bott in [14] established the famous iteration formula of the Morse index for closed geodesics on Riemannian manifolds. For convex Hamiltonian systems, Ekeland developed the similar Bott-type iteration index formulas for the Ekeland index theory (cf. [15] of 1990). In 1999 (cf. [16]), Long established the Bott-type iteration formulas for the Maslov-type index theory. Motivated by the above results, in [13] of Liu and Zhang in 2014, the following Bott-type iteration formulas for the L0-index was established.

    Definition 2.2. (cf. [13]) Given an τ>0, a positive integer k and a path γPτ(2n), the k-th iteration γk of γ in brake orbit boundary sense is defined by ˜γ|[0,kτ] with

    ˜γ(t)={γ(t2jτ)(γ(2τ))j,t[2jτ,(2j+1)τ],jN{0},  Nγ((2j+2)τt)N(γ(2τ))j+1,t[(2j+1)τ,(2j+2)τ],jN{0},

    where γ(2τ):=Nγ(τ)1Nγ(τ). $

    Theorem 2.1. (cf. [13] of Liu and Zhang in 2014) Suppose γPτ(2n), for the iteration symplectic paths γk, when k is odd, there hold

    iL0(γk)=iL0(γ1)+k12i=1iω2ik(γ2),νL0(γk)=νL0(γ1)+k12i=1νω2ik(γ2);

    when k is even, there hold

    iL0(γk)=iL0(γ1)+iL01(γ1)+k21i=1iω2ik(γ2),νL0(γk)=νL0(γ1)+νL01(γ1)+k21i=1νω2ik(γ2),

    where ωk=eπ1/k, and (iω,νω) is the ω-index pair defined by Long(cf. [16]).

    Proof of Theorem 1.1. For any BLs(R2n) satisfying condition (BS1), as in [1], we define

    E={xW1/2,2(S1,R2n)x(t)=Nx(t),a.e.tR}. (2.7)

    Equip E with the usual W1/2,2 norm. Then E is a Hilbert space with the associated inner product ,. We define two self adjoint liner operators A, B from E to E by

    Ax,y=10(J˙x,y)dt,Bx,y=10(B(t)x,y)dt,x,yE. (2.8)

    We also define

    ˆE={xW1/2,2(S1,R2n)x(t)=Nx(t),x(t+12)=x(t),a.e.tR}. (2.9)

    Equip ˆE with the usual W1/2,2 norm. We define two self adjoint liner operators ˆA, ˆB from ˆE to ˆE by

    ˆAx,y=10(J˙x,y)dt,ˆBx,y=10(B(t)x,y)dt,x,yˆE. (2.10)

    Then ˜E and ˆE are both subspaces of E and A invariant, we have both the A orthogonal and B orthogonal decomposition

    E=˜EˆE,˜A=A|˜E,ˆA=A|ˆE.

    Since B satisfies condition (BS1), one can verify the following orthogonal decomposition

    B=˜BˆB,˜B=B|˜E,ˆB=B|ˆE.

    Then we have the orthogonal decomposition

    M(AB)=M(˜A˜B)M(ˆA^B),for=±,0,

    where M(AB)E, M(˜A˜B)˜E, M(ˆAˆB)ˆE. So by the definitions of I(B1,B2), I(˜B1,˜B2), I(ˆB1,ˆB2) we have

    I(B1,B2)=I(˜B1,˜B2)+I(ˆB1,ˆB2), (2.11)

    where I(B1,B2) and I(ˆB1,ˆB2) are defined similarly as (1.3).

    Since B satisfies condition (BS1), one has BC(S1/2,Ls(R2n)). Thus

    ˆAx,y=2120(J˙x,y)dt,ˆBx,y=2120(B(t)x,y)dt,x,yˆE.

    So by Theorem 1.2 of [1] and (2.8) we have

    I(ˆB1,ˆB2)=iL0(γB2(t),[0,14])iL0(γB1(t),[0,14])ν1(γB1(14)) (2.12)

    Also by Theorem 1.2 of [1] and (2.8) we have

    I(B1,B2)=iL0(γB2(t),[0,12])iL0(γB1(t),[0,12])ν1(γB1(12)) (2.13)

    Since both B1 and B2 satisfy condition (BS1), for j=1,2, by Theorem 2.3 we have

    iL0(γBj(t),[0,12])=iL0(γBj(t),[0,14])+iL01(γBj(t),[0,14]), (2.14)
    νL0(γBj(t),[0,12])=νL0(γBj(t),[0,14])+νL01(γBj(t),[0,14]). (2.15)

    By (2.11)–(2.15) one has

    I(˜B1,˜B2)=iL01(γB2(t),[0,14])iL01(γB2(t),[0,14])νL01(γB2(t),[0,14]). (2.16)

    Thus Theorem 1.1 holds by the definitions of (iL01(γB),νL01(γB)) in (1.3).

    In this section we prove Theorem 1, 2.

    We study the 1-periodic brake orbit solution of Hamiltonian system (1.1)-(1.2)

    ˙x=JH(t,x),x(t+1)=x(t),x(12+t)=Nx(12t).

    It is well know that x is a solution of (1.1)-(1.2) if and only if it is a critical point of the functional f defined on ˜E as follows

    f(x)=12˜Ax,x+˜Φ(x),x˜E, (3.1)

    where ˜E is defined by (1.5), ˜A is defined in (1.6), ˜Φ(x)=10H(t,x)dt. It is easy to check that ˜Φ(x) is compact.

    In [17], Benci proved the following important abstract theorem:

    Theorem 3.1. Let fC1(E,R) have the form (3.1) and satisfy

    (f1) Every sequence {uj} such that f(uj)c<˜Φ(0) and ||f(uj)||0 as j+ is bounded.

    (f2) ˜Φ(u)=˜Φ(u), u˜E.

    (f3) There are two closed subspaces of ˜E, E+ and E, and a constant ρ>0 such that

    (a) f(u)>0 for uE+, where c0<c<˜Φ(0) be two constants.

    (b) f(u)<c<˜Φ(0) for uESρ, (Sρ={uE|||u||=ρ}).

    Then the number of pairs of nontrivial critical points of f is greater than or equalto dim(E+E)cod(E+E+). More over, the corresponding critical values belong toto [c0,c].

    Proof of Theorem 1.2. We take the method in [1,5] to prove this theorem.

    We set ˜E+=M+(˜A˜B) and ˜E=M(˜AB0). By Definition 1.1 and Theorem 1.2, we have

    dim(˜E+˜E)cod(˜E+˜E+)=dim(M+(˜A˜B)M0(˜A˜B0))dim((M(˜A˜B)M(˜A˜B))(M+(˜A˜B0)M0(˜A˜B0)))=I(˜B,˜B0)=iL01(˜B0)iL01(˜B). (3.2)

    Here ˜B0 and ˜B are compact operators from ˜E to ˜E defined by (1.6). Since 0 is an isolated eigenvalue of ˜A with n-dimensional eigenspace ˜E0, by (4-4) of [17], there exist two real numbers α<0 and β>0 such that

    ˜A˜B0u,uα||u||2,u˜E, (3.3)
    ˜A˜Bu,uβ||u||2,u˜E+. (3.4)

    Define

    V(t,x)=H(t,x)12˜B(t)x,x,V0(t,x)=H(t,x)12˜B0(t)x,x, (3.5)

    and let g(x)=10V(t,x)dt and g0(x)=10V0(t,x)dt, then we have

    f(x)=12(˜A˜B)x,xg(x),x˜E, (3.6)
    f(x)=12(˜A˜B0)x,xg0(x),x˜E. (3.7)

    By (H1)-(H2) and the same arguments in the proof of Lemma 5.5 of [17], we get

    lim||x||+||g(x)||||x||=0, (3.8)
    lim||x||+0||g0(x)||||x||=0. (3.9)

    So by definition of g0 and (3.9), we have

    g0(u)=˜Φ(0)+o(||u||2),for||u||0. (3.10)

    By (3.3) and (3.10) we have

    f(u)α||u||2+˜Φ(0)+o(||u||2),foruEand||u||0. (3.11)

    Since α<0, there exist a constant ρ>0 and γ1<0 such that

    f(u)<γ1+˜Φ(0),uESρ. (3.12)

    Setting c=γ12+˜Φ(0), (f3)(b) of Theorem 3.1 is satisfied.

    By (H2) for there exist M>0 such that

    |V(t,x)|β2|x|2+M|x|,xR2n. (3.13)

    Thus

    |g(u)|=|10V(t,u)dt|10|V(t,u)|dt10β2|u|2+M|u|β2||u||2+M||u||. (3.14)

    Then by (3.4) and (3.14), for every u˜E+, we get

    f(u)=12(˜A˜B)u,u+g(u)β||u||2|g(u)|β2||u||2M||u||. (3.15)

    This implies that f is bounded from below on ˜E+ and we can set

    c0=infuE+f(u)wwithw>0suchthatc0<c.

    Thus (f3)(a) of Theorem 3.1 is satisfied.

    Since ν1(˜B)=0, M0(˜A˜B)=0. Now we prove that (f1) is satisfied. other wise we can suppose ||uj||+ as j+, then by (3.6) and (3.8) we have

    0=limj+f(uj)=limj+((˜A˜B)uj+g(uj))=limj+(˜A˜B)uj. (3.16)

    But by (4-4) of [17] there exists a real number α>0 such that

    ||(˜A˜B)u||α||u||,uE. (3.17)

    Hence by (3.17) we have

    limj+||(˜A˜B)uj||=+, (3.18)

    which contradicts (3.16). This proves (f1) in Theorem 3.1.

    (H3) implies (f2) of Theorem 3.1 holds. Hence by Theorem 3.1, (1.1)-(1.2) has at least iL01(˜B0)iL01(˜B) pairs of nontrivial solutions whenever iL01(˜B0)iL01(˜B)>0. If iL01(˜B0)iL01(˜B)>0, we replace f by f and let E+=M(˜A˜B) and E=M+(˜A˜B0). By almost the same proof we can show that (f1)-(f3) of Theorem 3.1 hold. And by Theorems 1.2 and 3.1 (1.1)-(1.2) has at least iL01(˜B)iL01(˜B0) pairs of nontrivial brake orbit solution. The proof of Theorem 1.3 is completed.

    Similarly to Theorems 1.4 and 1.5 of [5] or [1]), we have

    Remark 3.1. If νL01(˜B)>0, we can prove (f1) of Theorem 3.1 under other additional conditions while we can prove (f2) and (f3) are satisfied under (H1)-(H3) by the same proof of Theorem 1.3.

    Suppose the following condition:

    (H4) V(t,x) is bounded and V(t,x)+ as |x|+, uniformly in t.

    By the proof of Theorem 5.2 of [17] and Theorem 4.1 of [18] (f1) holds.

    Suppose the following conditions:

    (H5) There is r>0 and p(1,2) such that

    pV(t,x)(z,V(t,x))>0for|z|r,tR.

    (H6) ¯lim|x||x|1|V(t,x)|c<12.

    (H7) There are constant a1>0 and a2>0 such that V(t,x)a|z|pa2.

    By the proof of Theorem 4.11 of [18] (f1) holds.

    Then under either additional condition (H4) or (H5)-(H7), (1.1)-(1.2) has at least iL01(˜B0)iL01(˜B)νL01(˜B) pairs of nontrivial solutions whenever iL01(˜B0)iL01(˜B)νL01(˜B)>0.

    This work is partially supported by the NSFC Grants 11790271 and 11171341, National Key R & D Program of China 2020YFA0713301, and LPMC of Nankai University. The authors sincerely thanks the referees for their careful reading and valuable comments and suggestions.

    The authors declare there is no conflict of interest.



    [1] M. I. Ali, F. Feng, X. Liu, W. K. Min, M. Shabir, On some new operations in soft set theory, Comput. Math. Appl., 57 (2009), 1547–1553. https://doi.org/10.1016/j.camwa.2008.11.009 doi: 10.1016/j.camwa.2008.11.009
    [2] M. I. Ali, A note on soft sets, rough soft sets and fuzzy soft sets, Appl. Soft Comput., 11 (2011), 3329–3332. https://doi.org/10.1016/j.asoc.2011.01.003 doi: 10.1016/j.asoc.2011.01.003
    [3] A. Ali, M. I. Ali, N. Rehman, New types of dominance based multi-granulation rough sets and their applications in conflict analysis problems, J. Intell. Fuzzy Syst., 35 (2018), 3859–3871. https://doi.org/10.3233/JIFS-18757 doi: 10.3233/JIFS-18757
    [4] S. Ayu, W. Mahmood, M. Shabir, A. N. Koam, R. Gul, A study on soft multigranulation rough sets and their applications, IEEE Access, 10 (2022). https://doi.org/10.1109/ACCESS.2022.3218695 doi: 10.1109/ACCESS.2022.3218695
    [5] M. Akram, F. Zafar, Multi-criteria decision-making methods under soft rough fuzzy knowledge, J. Intell. Fuzzy Syst., 35 (2018), 3507–3528. https://doi.org/10.3233/JIFS-18017 doi: 10.3233/JIFS-18017
    [6] N. Bhardwaj, P. Sharma, An advanced uncertainty measure using fuzzy soft sets: Application to decision-making problems, Big Data Min. Anal., 4 (2021), 94–103. https://doi.org/10.26599/BDMA.2020.9020020 doi: 10.26599/BDMA.2020.9020020
    [7] R. Chattopadhyay, P. P. Das, S. Chakraborty, Development of a rough-MABAC-DoE-based metamodel for supplier selection in an iron and steel industry, Oper. Res. Eng. Sci. Theor. Appl., 5 (2022), 20–40. https://doi.org/10.31181/oresta190222046c doi: 10.31181/oresta190222046c
    [8] D. Dubois, H. Prade, Rough fuzzy sets and fuzzy rough sets, Int. J. Gen. Syst., 17 (1990), 191–209.
    [9] E. Durmić, Z. Stević, P. Chatterjee, M. Vasiljević, M. Tomašević, Sustainable supplier selection using combined FUCOM-Rough SAW model, Rep. Mech. Eng., 1 (2020), 34–43. https://doi.org/10.31181/rme200101034c doi: 10.31181/rme200101034c
    [10] J. Din, M. Shabir, Y. Wang, Pessimistic multigranulation roughness of a fuzzy set based on soft binary relations over dual universes and its application, Mathematics, 10 (2022), 541. https://doi.org/10.3390/math10040541 doi: 10.3390/math10040541
    [11] J. Din, M. Shabir, S. B. Belhaouari, A novel pessimistic multigranulation roughness by soft relations over dual universe, AIMS Math., 8 (2023), 7881–7898. https://doi.org/10.3934/math.2023397 doi: 10.3934/math.2023397
    [12] E. S. T. E. Din, H. Anis, M. Abouelsaad, A probabilistic approach to exposure assessment of power lines electric field, IEEE T. Power Deliver., 20 (2005), 887–893. https://doi.org/10.1109/TPWRD.2005.844268 doi: 10.1109/TPWRD.2005.844268
    [13] E. T. Eldin, A new algorithm for the classification of different transient phenomena in power transformers combining wavelet transforms and fuzzy logic, In 2003 46th Midwest Symposium on Circuits and Systems, IEEE, 3 (2003), 1116–1121.
    [14] F. Feng, M. I. Ali, M. Shabir, Soft relations applied to semigroups, Filomat, 27 (2013), 1183–1196. https://doi.org/10.2298/FIL1307183F doi: 10.2298/FIL1307183F
    [15] Z. Fariha, M. Akram, A novel decision making method based on rough fuzzy information, Int. J. Fuzzy Syst., 20 (2018), 1000–1014. https://doi.org/10.1007/s40815-017-0368-0 doi: 10.1007/s40815-017-0368-0
    [16] F. Feng, C. Li, B. Davvaz, M. I. Ali, Soft sets combined with fuzzy sets and rough sets: A tentative approach, Soft Comput., 14 (2010), 899–911. https://doi.org/10.1007/s00500-009-0465-6 doi: 10.1007/s00500-009-0465-6
    [17] F. Feng, Y. B. Jun, X. Liu, L. Li, An adjustable approach to fuzzy soft set based decision making, J. Comput. Appl. Math., 234 (2010), 10–20. https://doi.org/10.1016/j.cam.2009.11.055 doi: 10.1016/j.cam.2009.11.055
    [18] J. Hou, Grey relational analysis method for multiple attribute decision making in intuitionistic fuzzy setting, J. Converg. Inf. Technol., 5 (2010), 194–199. https://doi.org/10.4156/jcit.vol5.issue10.25 doi: 10.4156/jcit.vol5.issue10.25
    [19] B. Huang, C. X. Guo, Y. L. Zhuang, H. X. Li, X. Z. Zhou, Intuitionistic fuzzy multigranulation rough sets, Inform. Sci., 277 (2014), 299–320. https://doi.org/10.1016/j.ins.2014.02.064 doi: 10.1016/j.ins.2014.02.064
    [20] S. S. Kumar, H. H. Inbarani, Optimistic multi-granulation rough set based classification for medical diagnosis, Proced. Comput. Sci., 47 (2015), 374–382. https://doi.org/10.1016/j.procs.2015.03.219 doi: 10.1016/j.procs.2015.03.219
    [21] M. J. Khan, P. Kumam, W. Deebani, W. Kumam, Z. Shah, Distance and similarity measures for spherical fuzzy sets and their applications in selecting mega projects, Mathematics, 8 (2020), 519. https://doi.org/10.3390/math8040519 doi: 10.3390/math8040519
    [22] M. J. Khan, , P. Kumam, N. A. Alreshidi, N. Shaheen, W. Kumam, Z. Shah, P. Thounthong, The renewable energy source selection by remoteness index-based VIKOR method for generalized intuitionistic fuzzy soft sets, Symmetry, 12 (2020), 977. https://doi.org/10.3390/sym12060977 doi: 10.3390/sym12060977
    [23] Z. Li, N. Xie, N. Gao, Rough approximations based on soft binary relations and knowledge bases, Soft Comput., 21 (2017), 839–852. https://doi.org/10.1007/s00500-016-2077-2 doi: 10.1007/s00500-016-2077-2
    [24] G. Liu, Rough set theory based on two universal sets and its applications, Knowl.-Based Syst., 23 (2010), 110–115. https://doi.org/10.1016/j.knosys.2009.06.011 doi: 10.1016/j.knosys.2009.06.011
    [25] C. Liu, D. Miao, N. Zhang, Graded rough set model based on two universes and its properties, Knowl.-Based Syst., 33 (2012), 65–72. https://doi.org/10.1016/j.knosys.2012.02.012 doi: 10.1016/j.knosys.2012.02.012
    [26] Z. Li, N. Xie, N. Gao, Rough approximations based on soft binary relations and knowledge bases, Soft Comput., 21 (2017), 839–852. https://doi.org/10.1007/s00500-016-2077-2 doi: 10.1007/s00500-016-2077-2
    [27] G. Lin, Y. Qian, J. Li, NMGRS: Neighborhood-based multigranulation rough sets. Int. J. Approx. Reason., 53 (2012), 1080–1093. https://doi.org/10.1016/j.ijar.2012.05.004 doi: 10.1016/j.ijar.2012.05.004
    [28] C. Liu, D. Miao, J. Qian, On multi-granulation covering rough sets, Int. J. Approx. Reason., 55 (2014), 1404–1418. https://doi.org/10.1016/j.ijar.2014.01.002 doi: 10.1016/j.ijar.2014.01.002
    [29] W. Ma, B. Sun, Probabilistic rough set over two universes and rough entropy, Int. J. Approx. Reason., 53 (2012), 608–619. https://doi.org/10.1016/j.ijar.2011.12.010 doi: 10.1016/j.ijar.2011.12.010
    [30] P. K. Maji, R. Biswas, A. R. Roy, Soft set theory, Comput. Math. Appl., 45 (2012), 555–562. https://doi.org/10.1016/S0898-1221(03)00016-6 doi: 10.1016/S0898-1221(03)00016-6
    [31] P. K. Maji, A. R. Roy, R. Biswas, An application of soft sets in a decision making problem, Comput. Math. Appl., 44 (2002), 1077–1083. https://doi.org/10.1016/S0898-1221(02)00216-X doi: 10.1016/S0898-1221(02)00216-X
    [32] P. K. Maji, R. Biswas, A. R. Roy, Fuzzy soft sets, J. Fuzzy Math., 9 (2001) 589–602.
    [33] D. Molodtsov, Soft set theory—first results, Comput. Math. Appl., 37 (1999), 19–31. https://doi.org/10.1016/S0898-1221(99)00056-5 doi: 10.1016/S0898-1221(99)00056-5
    [34] D. Meng, X. Zhang, K. Qin, Soft rough fuzzy sets and soft fuzzy rough sets, Comput. Math. Appl., 62 (2011), 4635–4645. https://doi.org/10.1016/j.camwa.2011.10.049 doi: 10.1016/j.camwa.2011.10.049
    [35] T. Mahmood, Z. Ali, Fuzzy superior mandelbrot sets, Soft Comput., 26 (2022), 9011–9020. https://doi.org/10.1007/s00500-022-07254-x doi: 10.1007/s00500-022-07254-x
    [36] T. Mahmood, Z. Ali, A. Gumaei, Interdependency of complex fuzzy neighborhood operators and derived complex fuzzy coverings, IEEE Access, 9 (2021), 73506–73521. https://doi.org/10.1109/ACCESS.2021.3074590 doi: 10.1109/ACCESS.2021.3074590
    [37] Z. Pawlak, Rough sets, Int. J. Comput. Inform. Sci., 11 (1982), 341–356. https://doi.org/10.1007/BF01001956 doi: 10.1007/BF01001956
    [38] Z. Pawlak, Rough sets: Theoretical aspects of reasoning about data, Kluwer Academic Publisher, 1991.
    [39] Y. Qian, J. Liang, Y. Yao, C. Dang, MGRS: A multi-granulation rough set, Inform. Sci., 180 (2010), 949–970. https://doi.org/10.1016/j.ins.2009.11.023 doi: 10.1016/j.ins.2009.11.023
    [40] A. R. Roy, P. K. Maji, A fuzzy soft set theoretic approach to decision making problems, J. Comput. Appl. Math., 203 (2007), 412–418. https://doi.org/10.1016/j.cam.2006.04.008 doi: 10.1016/j.cam.2006.04.008
    [41] M. Shabir, M. I. Ali, T. Shaheen, Another approach to soft rough sets, Knowl.-Based Syst., 40 (2013), 72–80. https://doi.org/10.1016/j.knosys.2012.11.012 doi: 10.1016/j.knosys.2012.11.012
    [42] M. Shabir, R. S. Kanwal, M. I. Ali, Reduction of an information system, Soft Comput., 2019, 1–13.
    [43] M. Shabir, J. Din, I. A. Ganie, Multigranulation roughness based on soft relations, J. Intell. Fuzzy Syst., 40 (2021), 10893–10908. https://doi.org/10.3233/JIFS-201910 doi: 10.3233/JIFS-201910
    [44] B. Sun, W. Ma, Multigranulation rough set theory over two universes, J. Intell. Fuzzy Syst., 28 (2013), 1251–1269. https://doi.org/10.3233/IFS-141411 doi: 10.3233/IFS-141411
    [45] B. Sun, W. Ma, X. Xiao, Three-way group decision making based on multigranulation fuzzy decision-theoretic rough set over two universes, Int. J. Approx. Reason., 81 (2017), 87–102. https://doi.org/10.1016/j.ijar.2016.11.001 doi: 10.1016/j.ijar.2016.11.001
    [46] B. Sun, W. Ma, Y. Qian, Multigranulation fuzzy rough set over two universes and its application to decision making, Knowl.-Based Syst., 123 (2017), 61–74. https://doi.org/10.1016/j.knosys.2017.01.036 doi: 10.1016/j.knosys.2017.01.036
    [47] B. Sun, W. Ma, X. Chen, X. Zhang, Multigranulation vague rough set over two universes and its application to group decision making, Soft Comput., 23 (2019), 8927–8956. https://doi.org/10.1007/s00500-018-3494-1 doi: 10.1007/s00500-018-3494-1
    [48] B. Sun, X. Zhou, N. Lin, Diversified binary relation-based fuzzy multigranulation rough set over two universes and application to multiple attribute group decision making, Inform. Fusion, 55 (2020), 91–104. https://doi.org/10.1016/j.inffus.2019.07.013 doi: 10.1016/j.inffus.2019.07.013
    [49] R. Sahu, S. R. Dash, S. Das, Career selection of students using hybridized distance measure based on picture fuzzy set and rough set theory, Decis. Mak. Appl. Manag. Eng., 4 (2021), 104–126. https://doi.org/10.31181/dmame2104104s doi: 10.31181/dmame2104104s
    [50] H. K. Sharma, A. Singh, D. Yadav, S. Kar, Criteria selection and decision making of hotels using Dominance Based Rough Set Theory, Oper. Res. Eng. Sci. Theor. Appl., 5 (2022), 41–55. https://doi.org/10.31181/oresta190222061s doi: 10.31181/oresta190222061s
    [51] E. E. S. Tag, Fault location for a series compensated transmission linebased on wavelet transform and an adaptive neuro-fuzzy inference system, In Electric Power Quality and Supply Reliability Conference (PQ), 2010,229–236.
    [52] A. Tan, W. Z. Wu, S. Shi, S. Zhao, Granulation selection and decision making with multigranulation rough set over two universes, Int. J. Mach. Learn. Cyb., 10 (2019), 2501–2513. https://doi.org/10.1007/s13042-018-0885-7 doi: 10.1007/s13042-018-0885-7
    [53] W. Z. Wu, J. S. Mi, W. X. Zhang, Generalized fuzzy rough sets, Inform. Sci., 151 (2003), 263–282. https://doi.org/10.1016/S0020-0255(02)00379-1 doi: 10.1016/S0020-0255(02)00379-1
    [54] W. Xu, W. Sun, Y. Liu, W. Zhang, Fuzzy rough set models over two universes, Int. J. Mach. Learn. Cyb., 4 (2013), 631–645. https://doi.org/10.1007/s13042-012-0129-1 doi: 10.1007/s13042-012-0129-1
    [55] W. Xu, X. Zhang, W. Zhang, Two new types of multiple granulation rough set, Int. Scholar. Res. Notices, 2013. https://doi.org/10.1155/2013/791356 doi: 10.1155/2013/791356
    [56] W. Xu, Q. Wang, X. Zhang, Multi-granulation fuzzy rough sets in a fuzzy tolerance approximation space, Int. J. Fuzzy Syst., 13 (2011).
    [57] W. Xu, Q. Wang, S. Luo, Multi-granulation fuzzy rough sets, J. Intell. Fuzzy Syst., 26 (2014), 1323–1340. https://doi.org/10.3233/IFS-130818 doi: 10.3233/IFS-130818
    [58] X. Yang, T.Y. Lin, J. Yang, Y. Li, D. Yu, Combination of interval-valued fuzzy set and soft set, Comput. Math. Appl., 58 (2009), 521–527. https://doi.org/10.1016/j.camwa.2009.04.019 doi: 10.1016/j.camwa.2009.04.019
    [59] R. Yan, J. Zheng, J. Liu, Y. Zhai, Research on the model of rough set over dual-universes, Knowl.-Based Syst., 23 (2010), 817–822. https://doi.org/10.1016/j.knosys.2010.05.006 doi: 10.1016/j.knosys.2010.05.006
    [60] X. B. Yang, X. N. Song, H. L. Dou, J. Y. Yang, Multi-granulation rough set: From crisp to fuzzy case, Ann. Fuzzy Math. Inform., 1 (2011), 55–70.
    [61] L. A. Zadeh, Fuzzy sets, Inform. Control, 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X doi: 10.1016/S0019-9958(65)90241-X
    [62] H. Y. Zhang, W. X. Zhang, W. Z. Wu, On characterization of generalized interval-valued fuzzy rough sets on two universes of discourse, Int. J. Approx. Reason., 51 (2009), 56–70. https://doi.org/10.1016/j.ijar.2009.07.002 doi: 10.1016/j.ijar.2009.07.002
    [63] K. Zhang, J. Zhan, W. Z. Wu, Novel fuzzy rough set models and corresponding applications to multi-criteria decision-making, Fuzzy Set. Syst., 383 (2020), 92–126. https://doi.org/10.1016/j.fss.2019.06.019 doi: 10.1016/j.fss.2019.06.019
    [64] C. Zhang, D. Li, R. Ren, Pythagorean fuzzy multigranulation rough set over two universes and its applications in merger and acquisition, Int. J. Intell. Syst., 31 (2016), 921–943. https://doi.org/10.1002/int.21811 doi: 10.1002/int.21811
    [65] C. Zhang, D. Li, Y. Mu, D. Song, An interval-valued hesitant fuzzy multigranulation rough set over two universes model for steam turbine fault diagnosis, Appl. Math. Model., 42 (2017), 693–704. https://doi.org/10.1016/j.apm.2016.10.048 doi: 10.1016/j.apm.2016.10.048
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