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The fuzzy metric space based on fuzzy measure

  • Jialiang Xie , Qingguo Li EMAIL logo , Shuili Chen and Huan Huang
Published/Copyright: September 20, 2016

Abstract

In this paper, we study the relation between a fuzzy measure and a fuzzy metric which is induced by the fuzzy measure. We also discuss some basic properties of the constructed fuzzy metric space. In particular, we show that the nonatom of fuzzy measure space can be characterized in the constructed fuzzy metric space.

MSC 2010: 54E70; 54A40; 60B05

1 Introduction

The theory of metric space is an important topic in topology. The methods of constructing a fuzzy metric have been extensively studied [14]. It is worth noting that George and Veeramani [5] introduced the concept of a fuzzy metric with the help of continuous t-norms. Despite being restrictive, this kind of fuzzy metric provides a more natural and intuitive way to connect with the metrizable topological spaces. This concept has been widely used in various papers devoted to fuzzy topology [511]. It also has been applied to color image filtering to improve image quality (see [9] and the references therein).

On the other hand, measure theory is one of the most important theories in mathematics and it has been extensively studied. The concept of fuzzy measure was first introduced by Sugeno [12]. It can be regarded as an extension of classical measure in which the additivity is replaced by a weaker condition, monotonicity. Klement et. al establish the axiomatic theory of fuzzy σ-algebras and develop a measure theory of fuzzy sets [1315]. So far, there are many different classes of fuzzy measures, such as possibility measure [16, 17], decomposable measure [1820], pseudo-additive measure [21, 22], and T-measure [2326] etc. A systematic study of fuzzy measure theory can be found in [2730].

Recently, the study of constructing a fuzzy metric using a fuzzy measure technique has been actively pursued. In particular, a fuzzy Prokhorov metric and ultrametric defined on the set of all probability measures in a compact fuzzy metric space have been developed in [31, 32]. Cao et. al [33] introduce fuzzy analogue of the Kantorovich metric among the set of possibility distributions. In [34, 35], the authors discuss the relations between the decomposable measure and the fuzzy metric. More specifically, it has been proven that, with a Hausdorff fuzzy pseudo-metric constructed on its power set, a stationary fuzzy ultrametric space can induce a σ-⊥-superdecomposable measure. The authors of [35] further use a topological approach to extend the t-conorm-based decomposable measures by introducing a fuzzy pseudometric structure on an algebra of sets. In [36] we constructed a pseudo-metric (in the sense of Pap) on the measurable sets of a given σ-⊥-decomposable measure, and then analyzed the connection between the induced pseudo-metric and the σ-⊥-decomposable measure.

In this paper we focus on the following problems: how to construct a fuzzy metric by using a fuzzy measure developed in [14, 15] and what is the relation between these two? And what is the relations between these two? Specifically, by introducing the concept of an equivalence relation on fuzzy measurable sets, we construct a fuzzy metric on the associated quotient sets from a given fuzzy measure. Furthermore, we study some basic properties of the constructed fuzzy metric space such as completeness and continuity. To gain better insight into our proposed method of constructing a fuzzy metric, we study the properties of the constructed fuzzy metric which can precisely reflect those of fuzzy measure. As an illustration we obtain that the nonatom of fuzzy measure space can be characterized in the constructed fuzzy metric space.

The rest of the paper is organized as follows. In Section 2, some basic notions and results are given. Sections 3 and 4 are devoted to constructing a fuzzy metric and discussing its properties. In Section 5, we discuss the relationships between the constructed fuzzy metric and the fuzzy measure. Finally, some concluding remarks are given in Section 6.

2 Preliminaries

We start this section by recalling the concept of triangular norms from [20, 37]. They are an important tool in extending the classical metric space to fuzzy metric space.

Definition 2.1 (Klement et al.)

A function ⊤ : [0, 1]2 → [0, 1] is called triangular norm (t-norm for short) if it satisfies the following conditions for all a, b, c, d ∈ [0, 1]:

  1. a⊤1 = a. (boundary condition)

  2. abcdwheneverac and bd.(monotonicity)

  3. ab = bb. (commutativity)

  4. a⊤(bc) = (ab)⊤c. (associativity)

A t-Norm ⊤ is said to be continuous if it is a continuous function in [0, 1]2. Typical examples of continuous t-Norms are the minimum TM, the product TP and the Łukasiewicz t-norm TL, which are given by, respectively:

TM(x, y)=min(x, y), TP(x, y)=xy, TL(x, y)=max(0, x+y1).

Because of the associative property, the t-Norm ⊤ can be extended by induction to n-ary operation by setting

i=1nxi=(i=1n1xi)xn

Due to monotonicity, for each sequence (xi)i∈ℕ of elements of [0, 1], the following limit can be considered:

i=1xi=limni=1nxi.

Next we recall the concept of a fuzzy metric with the help of the continuous t-norm, which is a generalization of the concept of Menger probabilistic metric to the fuzzy setting.

Definition 2.2 (George and Veeramani [5])

The 3-tuple (X, M, ⊤) is said to be a fuzzy metric space if X is an arbitrary nonempty set, ⊤ is a continuous t-norm and M is a fuzzy set on X2 × (0, ∞) satisfying the following conditions, for all x, y, z ∈ X, t, s > 0:

  1. M(x, y, t) > 0,

  2. M(x, y, t) = 1 iff x = y,

  3. M(x, y, t) = M(x, y, t)

  4. M(x, z, t + s) ≥ M(x, y, t)⊤M(y, z, t)

  5. M(x, y, ·) : (0, ∞) → (0, 1] is continuous.

If the condition (GV2) is replaced by the condition (GVp): M(x, x, t) = 1, then (X, M, ⊤) is said to be a fuzzy pseudometric space.

It was proved in [5] that in a fuzzy metric space X, the function M(x, y, ·) is nondecreasing for all x, yX. A sequence (xi)i∈ℕ in a fuzzy metric space (X, M, ⊤) is said to converge [6] to x if limi→∞M(xi, x, t) = 1 for all t > 0; a sequence (xi)i∈ℕ in a fuzzy metric space (X, M, ⊤) is said to be Cauchy [6] if limi, j →∞M(xi, x, t) = 1 for all t > 0; (X, M, ⊤) is said to be complete [8] if every Cauchy sequence is convergent. A mapping f from a fuzzy metric space (X, M, ⊤1) to a fuzzy metric space (X, M, ⊤2) is called uniformly continuous [7] if for each ε ∈ (0, 1) and each t > 0, their exist r ∈ (0, 1) and s > 0 such that N(f(x), f(y), t) > 1 – ε whenever M(x, y, s) > 1 – r.

In this follow-up, we recall several concepts from the measure theory of fuzzy sets (see e.g. [1315]).

Definition 2.3

Let X be a nonempty set, I the unit interval [0, 1]. A subsetAof IX is a fuzzy σ-algebra iff

  1. 0, 1A,

  2. AAimplies1 — A ∈ A,

  3. if{Ai}i=1is a sequence inA, theni=1Ai = sup AiA.

Definition 2.4

A finite fuzzy measure (or F-measure) on a fuzzyσ-algebraAis a function μ : A → [0, ∞) satisfying:

  1. μ(0) = 0,

  2. for AA, μ(1 – A) = μ(1) – μ(A),

  3. for A, BA, μ(A ∨ B) + μ (AB) = μ(A) + μ(B),

  4. if{Ai}i=1is a sequence inAsuch thatAiA, AA, thenμ(A) sup = μ(Ai).

We call (X, A, μ) an F-measure space, elements of A are referred as fuzzy measurable sets.

3 Constructing fuzzy metric based on F-measure

In this follow-up, ⊤ stands for the minimum t-norm TM. The following result is the natural fuzzy metric structure on fuzzy measurable sets.

Theorem 3.1

Let (X, A, μ) be an F-measure space. If we define the fuzzy set M on A2 × (0, ∞) by

M(A, B, t)=tt+μ(AB)μ(AB),

where A, B ∈ A. Then M is a fuzzy pseudometric on A.

Proof

From the definition of M, it is obvious that for any A, BA, t > 0, we have (i) M(A, B, t) > 0; (ii) M(A, A, t) = 1 and M(A, B, t) = M(B, A, t); (iii) M(x, y, ·) : (0, ∞) → [0, 1] is continuous. The only thing that we need to prove is the triangular inequality. For any A, B, C ∈ A and t, s > 0, we have M(A, B, t)=tt+μ(AB)μ(AB), M(B, C, s)=ss+μ(BC)μ(BC) and M(A, C, t+s)=t+st+s+μ(AC)μ(AC). We are going to verify M(A, B, C, t + s) ≥ M(A, B, t) ⊤ M(B, C, s). For each A, B, C, ∈ A, we have

μ(AB)+μ(BC)+μ(AC)=μ(ABC)+μ(AB)(BC)+μ(AC)=μ(ABC)+μB(AC)+μ(AC)μ(AC)+μB(AB)C+μ(ABC)=μ(AC)+μ(AB)+μ(BC)

and so

μ(AC)μ(AC)μ(AB)μ(AB)+μ(BC)μ(BC)

we can distinguish two cases: M(B, C, s) ≥ M(A, B, s) or M(A, B, t) ≥ M(B, C, s).

Case (i). If M(B, C, s) ≥ M(A, B, t), or equivlently,

ss+μ(BC)μ(BC)tt+μ(AB)μ(AB)

and hence

s(μ(AB)μ(AB))t(μ(BC)μ(BC))

Consequently,

t(μ(AC)μ(AC))t(μ(AB)μ(AB))+t(μ(BC)μ(BC))t(μ(AB)μ(AB))+s(μ(AB)μ(AB)).

This implies that

tt+μ(AB)μ(AB)t+st+s+μ(AC)μ(AC),

and hence

M(A, C, t+s)M(A, B, t)=M(A, B, t)M(B, C, s).

Case (ii). Similar to (i). Thus, M is a fuzzy pesudometric on A.

Remark 3.2

Based on Theorem 3.1, M is a fuzzy pseudometric onAunder any left-continuous t-norms since TM is the biggest left-continuous t-norm.

Generally speaking, the fuzzy pseudometric space ( A, M, ⊤) is usually not a fuzzy metric space. But we can construct a fuzzy metric space from a fuzzy pseudometric metric space ( A, M, ⊤) and, at the same time, keep the general characteristics of the fuzzy pseudometric metric space.

Lemma 3.3

Let (X, A, μ) be an F-measure space. For each A, B ∈, we define the relation “~” on A : A ~ B if and only if μ(AB) = μ(AB). Then ~ is an equivalence relation on A.

Proof

The identity μ, (AC) - μ, (AC) ≥ μ(AB) - μ(AB) + μ(BC) μ(BC) - which was proved in Theorem 3.1, and the monotonicity of μ imply that ∼ is transitive, and it is clearly reflexive and symmetric.

Theorem 3.4

Let (X, A, μ) be an F-measure space. LetA/μ be the set of all equivalence classes for the relation “∼”. The fuzzy pseudometric M has a natural extension to

M([A], [B], t)=M(A, B, t),

where [A] ([B]) denote the equivalence class of A (B). Then M is a fuzzy metric onA/μ.

Proof

We first prove that M([A], [B], t) is well defined on A/μ. If A1 ∈ [A] and B1 ∈ [B], we conclude that μ(AB1) - μ(AB1) = μ(AB) - μ(AB). In fact

μ(A1B1)μ(A1B1)μ(A1A)μ(A1A)+μ(AB)μ(AB)+μ(BB1)μ(BB1).

Since A1 ∈ [A] and B1 ∈ [B], we have μ(A1A) - μ(A1A) = μ(BB1) - μ(BB1) = 0, then μ(A1B1) - μ(A1B1) ≤ μ(AB) - μ(AB). In a similar way that μ(AB) - μ(AB) ≤ μ(A1B1) - μ(A1B1) holds. We obtain M(A1, B1, t) = M(A, B, t), which shows that M([A] [B], t) does not depend on our choice of representatives in the equivalence classes and is well-defined on A/μ. Secondly, we need to prove that M is a fuzzy metric on A/μ. It is obvious that M([A] [B], t) = 1 ⇔ μ(AB) - μ(AB) = 0 ⇔ [A] = [B]. Thus, M is a fuzzy metric on A/μ.

The following Lemma shows that the collection of equivalence class A/μ forms a fuzzy σ-algebra.

Lemma 3.5

Let (XAμ) be an F -measure space. Then forAi, BiA, AiBi, i ∈ ℕ, we have

  1. 1 - Ai1 - Bi

  2. A1A2 ~ B1B2andA1A2 ~ B1B2

  3. i=0Aii=1Bi.

Proof

  1. For Ai, BiA, AiBi, then μ(AiBi) - μ(AiBi) = 0. Hence, 1 - Ai1 - Bi holds by μ((1Ai)(1Bi))μ((1Ai)(1Bi))=μ(AiBi)μ(AiBi)=0

  2. We first show that for each PA, A1PB1P. Since A1B1, we have

    μ(A1∨!P)μ(A1B1P)=μ(A1B1)+μ(P)μ(A1B1)Pμ(B1)+μ(P)μ(B1P)=μ(B1P).

    Similarly,

    μ(B1P)μ(A1B1P)μ(A1P).

    Consequently,

    μ(A1P)=μ(A1B1P)=μ(B1P).

    Thus we obtain

    μ(A1A2)=μ(B1B2)=μ(A1A2B1B2).

    This implies that A1A2B1B2. Similarly, we have A1A2B1B2.

  3. For i ∈ ℕ, we have μ(AiBi) = μ(AiBi), then μ(Ai) = μ(Bi) = μ(AiBi). Hence

    μ(i=1Ai)=limkμ(i=1kAi)=limkμ(i=1kBi)=μ(i=1Bi).

    Also,

    μ(i=1Ai)=limkμ(i=1kAi)=limkμ(i=1k(AiBi))=μ(i=1(AiBi))=μ((i=1Ai)(i=1Bi)).

    Thus, i=1Aii=1Bi.

Lemma 3.6

Let (X, Aμ) be an F-measure space. If, forAi, BiA, AiBi, i ∈ ℕ, AiA, BiBandthenAB.

Proof

The proof is straightforward.

According to Lemma 3.5 and Lemma 3.6, by means of representatives of classes, we can introduce the operations of union, intersection and complementation on A/μ:i=1[Ai]=[i=1Ai];i=1[Ai][i=1Ai];1[Ai]=[1Ai], [Ai] ∈ A/μ denote the equivalence class of Ai in A. Hence, A/μ is a fuzzy σ algbra. We therefore properly define μ on A/μ by setting

μ([A])=μ(A),forallAA.

The pair (A/μ, μ) is a said to be an F-measure algebra.

For convenience and simplicity, we denote members [A] of A/μ by A, and functions μ: A/μ → [0, ∞) by μ: A/μ → [0, ∞)

4 Properties of the fuzzy metric space( A, M, ⊤)

In this section, we study some properties of the fuzzy metric space ( A, M, ⊤) based on F -measure µ.

Theorem 4.1

Let (X, A, µ) be an F -measure space and M be the fuzzy metric on A defined in Theorem 3.4. Then the maps (i) (A, B) ↦ AB and (ii) (A, B) ↦ AB are uniformly continuous fromA × AtoA.

Proof

  1. For any A1, B1, A2, B2, t, s > 0, we first prove the relation M(A1, A2, t) ⊤ M(B1, B2, s) ≤ M(A1B1, A2B2, t + s). For each A1, B1, A2, B2A, we have

    μ(A1B1)(A2B2)μ(A1B1)(A2B2)μ(A1A2)(B1B2)μ(A1A2)(B1B2)=μ(A1A2)+μ(B1B2)μ(A1A2)(B1B2)μ(A1A2)μ(B1B2)+μ(A1A2)(B1B2)μ(A1A2)μ(A1A2)+μ(B1B2)μ(B1B2).

    we can distinguish two cases: M(A2, B2, s) ≥ M(A1, B1, t) or M(A1, B1, t) ≥ M(A2, B2, s).

    Case one. If M(B1, B2, s) ≥ M(A1, A2, t), or equivlently,

    ss+μ(B1B2)μ(B1B2)tt+μ(A1A2)μ(A1A2)

    and hence

    s(μ(A1A2)μ(A1A2))t(μ(B1B2)μ(B1B2))

    In consequence,

    tμ(A1B1)(A2B2)μ(A1B1)(A2B2)tμ(A1A2)μ(A1A2)+tμ(B1B2)μ(B1B2)tμ(A1A2)μ(A1A2)+sμ(A1A2)μ(A1A2)

    This implies that

    tt+μ(A1A2)μ(A1A2)t+st+s+μ((A1B1)(A2B2))μ((A1B1)(A2B2))

    and hence

    M(A1B1, A2B2, t+s)M(A1, A2, t)=M(A1, A2, t)M(B1, B2, s).

    Case two. Similar to case one.

    Thus, M(A1B1, A2B2, t + s) ≥ M(A1, A2, t)⊤M(B1, B2, s).

    If we fix ε ∈ (0, 1), there exists δ ∈ (0, 1) such that (1 -δ)⊤(1-δ) > 1 - ε, by the continuity of ⊤. Thus, for each ε ∈ (0, 1) there exist δ ∈ (0, 1) such that M(A1B1, A2B2, t + s) > 1-ε whenever M(A1, A2, t) > 1-δ and M(B1, B2, s) > 1-δ. We conclude that (A, B) ↦ AB is uniformly continuous.

  2. Proceeding as in the proof of (i), we prove the relation M(A1B1, A2B2, t + s) ≥ M(A1, A2, t)⊤M(B1, B2, s), for any A1, B1, A2, B2A, t, s > 0. Moreover, we conclude that (A, B) ↦ AB is uniformly continuous by using similar technique in (i).

Now, we shall consider the completeness of the fuzzy metric space A, M, ⊤.

Lemma 4.2

Let (X, A, µ) be an F -measure space and Ai (i = 1, 2, ...) be elements ofA. Then the fuzzy metric M as defined in Theorem 3.4 satisfies the following properties:

  1. M(A1, A2, t)M(A1, A1A2, t)for each t > 0;

  2. M(Ak, Ak+1, tk)M(i=1kAi, i=1k+1Ai, i=1kti), for all k ∈ ℕ, ti > 0.

Proof

  1. For any A1, A2A, we get

    μA1(A1A2)μA1(A1A2)=μ(A1A2)μ(A1)μ(A1A2)μ(A1A2)

    and so M(A1, A2, t) ≤ M(A1, A1A2, t), for each t > 0.

  2. By (i) and Theorem 4.1 (i), for all k ∈ ℕ, ti > 0, we have

    M(ki=1Ai,k+1i=1Ai,i=1kti)=M((k1i=1Ai)Ak,(k1i=1Ai)(AkAk+1),i=1kti)M(k1i=1Ai,k1i=1Ai,i=1k1ti)M(Ak,AkAk+1,tk)1M(Ak,Ak+1,tk)=M(Ak,Ak+1,tk)

Lemma 4.3

Let (xn)n∈ℕbe a Cauchy sequence in fuzzy metric space (x, M, ⊤). If there is a subsequence (xk(n)n∈ℕof (xn)n∈ℕthat converges to x in X, then the Cauchy sequence (xn)n∈ℕconverges to x.

Proof

Let (xk(n))n∈ℕ be a subsequence of (xn)n∈ℕ Then, given r with 0 < r < 1 and t > 0, there is an n0 ∈ ℕ such that for each nn0, M(x, xk(n), t2)>1s, where s > 0 satisfies (1-s)⊤(1-s) > 1-r. Since (xn)n∈ℕ be a Cauchy sequence, there is n1k(n0) such that M(xn, xm, t2)>1s for each n, mn1. Therefore, for each nn1, we have

M(x, xnt)M(x, xk(n), t2)M(x, xk(n), t2)(1s)(1s)>1r.

We conclude that the Cauchy sequence (xn)n∈ℕ converges to x.

Theorem 4.4

The fuzzy metric space (A, M, ⊤) based on F -measure µ is complete.

Proof

Let (An)n∈ℕ be a Cauchy sequence in A. We need to show that there exists a set AA such that limnM(An, A, t)=1 for each t > 0. By Lemma 4.3, it suffices to show that there is a convergent subsequence in (An)n∈ℕ. Hence, passing to subsequence, there exists n0 ∈ ℕ such that M(An, An+1, tn) > 1-2−n for all nn0, tn > 0.

For n ∈ ℕ, let Bp=i=nn+pAi. Then (Bp) is a monotonic increasing sequence and limpBp=i=nAiDn so, μ(Dn)=limpμ(Bp). By Lemma 4.2, we have

    M(An, Bp, ptn+(p1)tn+1++tn+p1)M(An, B1, tn)M(B1, B2, tn+tn+1)M(Bp1, Bp, tn+tn+1++tn+p1)M(An, An+1, tn)M(An+1, An+2+tn+1)M(An+p1, +An+p+tn+p1)>(12n)(12n1)(12np+1)

Hence, limnlimpM(An, Dn, ptn+(p1)tn+1++tn+p1)=1. Moreover, since the sequence (Dn)n∈ℕ is monotonic decresing, we can set A=n=1Dn, t=limnlimp(ptn+(p1)tn+1++tn+p1), AA in the light of A is a fuzzy σ-algebra. Thus, limnM(An, A, t)=1 and complete the proof.

5 The correspondence between fuzzy metric space and F-measure space

In the following, we give the characteristics of the nonatom of the F-measure algebra (A, µ).

Definition 5.1

If, for two distinct elements A, B ⊂ X, there exists t, s > 0 and an element P ⊂ X, different from both A and B such that M(A, B, t + s) = M(A, P, t)⊤M(P, B, s), then fuzzy metric space (X, M, ⊤) is called convex..

Definition 5.2

Let (A, µ) be an F-measure algebra. Then the measure µ is called nonatom if for A, B ∈ A, A ≤ B and µ(A) < µ(B), there exists P ∈ A, A ≤ P ≤ B such that µ(A) < µ(P) <(B).

Theorem 5.3

The F-measure algebra (A, µ) is nonatom if and only if fuzzy metric space (A, M, ⊤) is convex.

Proof

(Sufficiency) Suppose that A is convex. Let A, CA with AC and µ(A) < µ(C). Since AC, there exists t, s > 0 and BA, which is different from both A and C such that M(A, C, t + s) = M(A, B, t)⊤M(B, C, s). We can distinguish two cases: M(A, B, t) ≥ M(B, C, s) or M(B, C, s) ≥ M(A, B, t). Since M is nondecreasing with respect to t, it is easy to verify that in the two cases the inequalities

μ(AC)μ(AC)μ(AB)μ(AB), μ(AC)μ(AC)μ(BC)μ(BC)()

hold. Set P = A ∨ (B ∧ C) = (A ∨ B) ∧ C, then A ≤ P ≤ C.

Suppose that µ(A ∧ B) = µ(B ∧ C). Then

μBCμBC=μBCμABμCμA=μACμAC

which contradicts the condition (*). Hence, we have μ(BC ) > μ(AB). Consequently, μ(P)- μ(A) = μ(BC)-μ(AB) > 0, i.e, μ(P) > μ(A).

On the other hand, suppose that μ(AB) = μ(BC). Then

μABμAB=μBCμABμCμA=μACμAC

which contradicts the condition (∗). Hence, we obtain μ(B ∨ C) > μ(A ∨ B). Consequently, μ(C)-μ(P) = μ(B ∨ C)-μ(A ∨ B) > 0, i.e, μ(C) > μ(P). Thus the F-measure μ is nonatom.

(Necessity) Suppose that μ is nonatom, A and CA with AC.

Case (i). If ACA and ACC, put B = AC. Then μ(A ∨ B)-μ(AB)+μ(B ∨ C)-μ(B ∧ C) = μ(A)-μ(A ∨ C)+μ(C)-μ(A ∧ C) = μ(A ∨ C)-μ(A ∧ C).

Case (ii). Suppose AC = A. Then μ(A ∧ C) = μ(A) and also μ(A ∨ C) = μ(C). We deduce that μ(A) < μ(C) in the light of (A, µ) is F-measure algebra. By the assumption, there exists BA, ABC such that μ(A) < μ(B) < (C). Hence μ(A ∨ B)-μ(A ∧ B)+μ(B ∨ C)-μ(B ∧ C) = μ(B)-μ(A)+μ(C)-μ(B) = μ(A ∨ C)-μ(A ∧ C). So, both in the case (i) and (ii) we deduce that

μ(AB)μ(AB)+μ(BC)μ(BC)=μ(AC)μ(AC).()

Next we need to prove that there exists t, s > 0, such that M(A, C, t + s) = M(A, B, t)⊤M(B, C, s). If there exists t, s > 0, such that

tt+μ(AB)μ(AB)=t+st+s+μ(AC)μ(AC),

so

μ(AB)μ(AB)=t(μ(AC)μ(AC))t+s,

according to the equality (∗∗), we get

μ(BC)μ(BC)=s(μ(AC)μ(AC))t+s,

and hence

ss+μ(BC)μ(BC)=t+st+s+μ(AC)μ(AC).

In consequence, M(A, C, t + s) = M(A, B, t)⊤M(B, C, s). Also, if there exists t, s > 0, such that

ss+μ(BC)μ(BC)=t+st+s+μ(AC)μ(AC),

we can get M(A, C, t + s) = M(A, B, t)⊤M(B, C, s) by the same method as employed in the above. This completes the proof of theorem.

6 Conclusion

In this paper, by constructing a fuzzy metric on the fuzzy measurable sets, we studied the relations between these two. In particular, several satisfactory properties of the constructed fuzzy metric have been obtained. In addition, we investigated the charaterization of the nonatom of the fuzzy measure and the corresponding properties of constructed fuzzy metric space. The main results and methods presented in this paper generalize some well known results in [38, 39].

Acknowledgement

The authors are enormously grateful to the editors and the anonymous reviewers for their professional comments and valuable suggestions. This work is supported in part by the National Natural Science Foundation of China (No. 11371130, 61103052, 11401195), the Natural Science Foundation of Fujian Province (No. 2014H0034, 2016J01022), the projects of Education Department of Fujian Province (No. JA15280) and Li Shangda Discipline Construction Fund of Jimei University.

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Received: 2016-5-14
Accepted: 2016-7-5
Published Online: 2016-9-20
Published in Print: 2016-1-1

© 2016 Xie etal., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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