Fuzzy Approximating Metrics, Approximating Parametrized Metrics and Their Relations With Fuzzy Partial Metrics

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Fuzzy Approximating Metrics, Approximating Parametrized Metrics and Their Relations With Fuzzy Partial Metrics

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Submitted:

04 July 2023

Posted:

11 July 2023

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Abstract

We generalize the concept of a fuzzy metric by introducing its approximating counterpart in order to make it more appropriate for the study of some problems related to words combinatorics. We establish close relations between fuzzy approximating metrics in case of special $t$-norms and approximating parametrized metrics, discuss some relations between fuzzy approximating metrics and fuzzy partial metrics as well as show some possible applications of approximating parametrized metrics in the problems of words combinatorics.

Keywords:

Subject: Computer Science and Mathematics - Geometry and Topology

0. Introduction

Recently some researchers have shown interest in the use of methods and tools of classical analysis, such as metrics and topologies, in the “non-traditional" areas, such as theoretical computer science, word combinatorics, data mining, etc. In particular, different metrics describing distance between infinite words, limits of sequences of words and topologies on the set of infinite words were studied (see e.g. [6]). However, usually there are not the classical metrics that provide effective tools for these studies, but more general structures such as fuzzy metrics, (see e.g. [1]), partial metrics (see, e.g. [20]), fuzzy partial metrics (see, e.g. [16], fuzzy fragmentary metrics [2], parametrized metrics [4,25] etc. In this paper, we introduce yet another generalization of a fuzzy metrics, called an fuzzy approximating metrics (Section 3) and a closely related with them the approximating version of parametrized metric (Section 4). We study properties of fuzzy approximating metrics and approximating parametrized metrics, discuss their relations with (fuzzy) partial metrics (Section 5) and consider the applicability of these concepts for the study of the problems related to words combinatorics (Section 6). We conclude the paper with Section 7 where some ideas for the future work in this field are discussed.

1. Preliminaries: Fuzzy metrics

In 1951, K. Menger [21] introduced the notion of a statistical metric. This concept was carefully studied and renamed as probabilistic metric in [23]. Later, basing on the definition of a probabilistic metric, I. Kramosil and J. Michalek [19] introduced the notion of a fuzzy metric. Basing on the Kramosil-Michalek’s definition, A. George and P. Veeramani [13,14] after some, outwardly not very significant modification, introduced an alternative concept known now as a GV-fuzzy metric. The informal difference between the two definitions is that in GV-definition a greater role is given to the fuzzy component compared to the probabilistic component, the influence of which is more noticeable in the original definition of Kramosil-Michalek. In the sequel, when dealing with fuzzy metrics, we always mean the George-Veeramani’s version. An interested reader can easily find counterpart of the study provided in this work for the Kramosil-Michalek’s fuzzy metrics.

The both definitions of fuzzy metric rely on the concept of a t-norm. The standard source for references on t-norms is the monograph [17]. However, for the readers convenience we give basic information about t-norms that will be needed in our work.

Definition 1.

A t-norm is a binary operation

* : [0, 1] \times [0, 1] \to [0, 1]

on the unit interval

[0, 1]

satisfying the following conditions for all

a, b, c

(1tn): * is monotone: $a \leq b \Rightarrow a * c \leq b * c$ ;
(2tn): * is commutative: $a * b = b * c$ ;
(3tn): * is associative: $(a * b) * c = a * (b * c)$ ;
(4tn): $a * 1 = a$ .

Example 1.

Among the most important examples of t-norms are the following four (see e.g. [17,23]).

Define $a * b : = a \land b$ where ∧ denotes the operation of taking minimum in [0,1]. It is called the minimum t-norm.
Define $a * b : = a \cdot b$ be the product. This is the so called product t-norm.
Define $a *_{L} b : = max (a + b - 1, 0) .$ This is the well-known ukasiewicz t-norm.
Define $a * b : = \frac{a \cdot b}{a + b - a \cdot b}$ This is known as the Hamacher t-norm.

Remark 1.

It is known and can be easily seen that

a \land b \geq a * b

for every t-norm *. Hence ∧ is the largest t-norm.

Definition 2.

[13] Let X be a set and

* : [0, 1] \times [0, 1] \to [0, 1]

a continuous t-norm. A fuzzy metric on a set X is a mapping

M : X \times X \times (0, \infty) \to [0, 1]

satisfying the following axioms:

(FM0): $M (x, y, t) > 0 \forall x, y \in X$ and $\forall t \in (0, \infty)$ ;
(FM1): $M (x, y, t) = 1$ if and only if $x = y$ ;
(FM2): $M (x, y, t) = M (y, x, t) \forall x, y \in X, \forall t \in (0, \infty)$ ;
(FM3): $M (x, z, t + s) \geq M (x, y, t) * M (y, z, s) \forall x, y, z \in X, \forall s, t \in (0, \infty)$ ;
(FM4): $M (x, y, -) : (0, \infty) \to [0, 1]$ is lower semicontinuous.1

In order to specify t-norm used in the definition of a fuzzy metric, we refer to a fuzzy metric as the pair

(M, *)

Note that from axioms (FM3) and (FM1) it follows that the mapping

M (x, y, -) : (0, \infty) \to [0, 1]

is non-decreasing.

In our work we apply also the following stronger version of a fuzzy metric first introduced in [22].

Definition 3.

A fuzzy metric M on a set X is called strong if in addition to axioms(FM0)-(FM2)the following modifications of axioms(FM3)and(FM4)are satisfied

(FKM3): $M (x, z, t) \geq M (x, y, t) * M (y, z, t)$ for all $x, y, z \in X$ and for all $t \in (0, \infty)$ .
(FKM4): $M (x, y, -) : R^{+} \to [0, 1]$ is left continuous and non-decreasing, (that is $t < s ⟹ M (x, y, t) \leq M (x, y, s), \forall x, y \in X .$ ).

Remark 2.

In case we just replace axiom (FKM3) by axiom (FKM3

^{s}

) the resulting concept may fail to be a fuzzy metric. The simplest example of such situation was found by A. Sapenas and S. Morillas [22]. Therefore in order to get a stronger version of a fuzzy metric one has either to add axiom (FKM3

^{s}

) to the definition of a fuzzy metric (as it is done in [22]) or to replace (FKM3) with (FKM3

^{s}

) and ask additionally that

M (x, y, -) : R^{+} \to [0, 1]

is non-decreasing, as we did in [15] and in this paper.

2. Fuzzy approximating metrics

Inspired by the works of Matthews and coauthors [5,18,20], et al., we realized certain inconsistency or discrepancy of our works in words combinatorics which are based on fuzzy metrics with the actual situation. When comparing two words in practice, usually the information is not available as given at present but appears only in the process of computation. We interpret this computation as the procedure along parameter

t \in (0, \infty)

, that is along the third argument in the definition of a fuzzy metric. Under this interpretation axiom (FM1) is too strong: given a string

x = (x_{0}, x_{1}, \dots, x_{n}, \dots)

at the stage

t \in (0, \infty)

we have information about this string only till

{⌊ t ⌋}^{t h}

coordinate and cannot yet confirm that

M (x, x, t) = 1

. On the other hand “at the infinity” we have information about all elements of the string and therefore it is natural to request that

{lim}_{t \to \infty} M (x, x, t) = 1

for every

x \in X

. Besides, when comparing x and y at every step

⌊ t ⌋

, thus having information up to t on the both strings and not knowing yet whether

x = y

we obviously have only relation

M (x, x, t) \geq M (x, y, t)

. We view these observations as justification for the following definitions first introduced in our paper [3].

Definition 4.

A fuzzy approximating metric on a set X is a mapping

M : X \times X \times (0, \infty) \to [0, 1]

satisfying the following axioms:

(FAM0): $M (x, y, t) > 0 \forall x, y \in X, \forall t \in (0, \infty)$ ;
(FAM1): $M (x, x, t) \geq M (x, y, t) \forall x, y \in X$ ;
(FAM2): If $x, y \in X$ then ${lim}_{t \to \infty} M (x, y, t) = 1$ if and only if $x = y$ ;
(FAM3): $M (x, y, t) = M (y, x, t) \forall x, y \in X, \forall t \in (0, \infty)$ ;
(FAM4): $M (x, z, t + s) \leq M (x, y, t) * M (y, z, s) \forall x, y, z \in X, \forall t, s \in (0, \infty)$ ;
(FAM5): $M (x, y, -) : (0, \infty) \to [0, 1]$ is left semicontinuous for all $x, y \in X .$

Remark 3.

Comparing Definition 4 and Definition 2 notice first that we revised axiom (FM1) by splitting it into two axioms (FAM1) and (FAM2); the intuitive meaning of this splitting is explained above. We do not revise axioms (FM2) and (FM3) that appear now as axioms (FAM3) and (FAM4) in Definition 4 since they reflect information at finite steps

{⌊ t ⌋}^{t h}

and hence are operating with already received information. We do not revise also axiom (FM4) that appear now as axiom (FAM5) since it is given in the global way, that is for each specific

t \in (0, \infty)

From axiom (FAM4) and taking into account axiom (FAM5) we get

Proposition 1.

M (x, y, -) : (0, \infty) \to [0, 1]

is a non-decreasing function and

{lim}_{t ↗ t_{0}} M (x, y, t) = M (x, y, t_{0}) .

Patterned after Definition 3 we introduce the strong version of the fuzzy approximating metric.

Definition 5.

A fuzzy approximating metric M on a set X is called strong if in addition to axioms(0FAM)-(3FAM), the following modifications of axioms(4FAM)and(5FAM)are satisfied

(4FAM): $M (x, z, t) \geq M (x, y, t) * M (y, z, t)$ for all $x, y, z \in X$ and for all $t \in (0, \infty)$ .
(5FAM): $M (x, y, -) : R^{+} \to [0, 1]$ is left continuous and non-decreasing, that is $t < s ⟹ M (x, y, t) \leq M (x, y, s) \forall x, y \in X .$

In the next sections of this paper we discover close relations between approximating parametrized metrics and fuzzy approximating metrics based on some particular t-norms, discuss some connections between fuzzy approximating metrics and fuzzy partial metrics and give an example of possible application of approximating parametrized metrics in words combinatorics.

3. Approximating parametrized metrics

Many researchers used families of (pseudo)metrics, often endowed with a parameter, in order to characterize objects of their study. For example, J.F. McClendon [7] considers a disjoint collection of metric spaces whose metrics are compatible with a given topology on the disjoint union of sets, V. Radu [9] applies families of metrics in the research of distribution functions, D. Schueth [10] in her research uses families of Rimannian metrics on simply connected manifolds, etc. On the other hand, we are aware of only a few studies in which just parametrized metrics were considered as objects. Actually, the first work known to us, in which parametrized metrics appear is a work by N. Hussein and co-authors [8]. Namely, a parametrized metric on a set X is a function

P : X \times X \times (0, \infty) \to [0, \infty)

such that

(PM1): $P (x, y, t) = 0$ for all $t > 0$ if and only if $x = y$ ;
(PM2): $P (x, y, t) = P (y, x, t)$ for all $x, y \in X$ and all $t > 0$ ;
(PM3): $P (x, z, t) \leq P (x, y, t) + P (y, z, t)$ for all $x, y, z \in X$ and all $t > 0$ .

(Note that in the paper cited above the authors use for this concept a very inappropriate, in our opinion, name of a parametric metric.)

In the paper [25] we presented a construction of a parametrized metric based on a appropriately chosen t-conorm. In this paper, in the spirit of this work, we introduce the approximating version for parametrized metrics.

Definition 6.

Let X be a set. A mapping

P : X \times X \times (0, + \infty) \to R^{+}

is called an approximating parametrized metric on X if it satisfies the following conditions:

(APM1): $P (x, x, t) \leq P (x, y, t) \forall x, y \in X, \forall t \in (0, + \infty)$ ;
(APM2): ${lim}_{t \to \infty} P (x, y, t) = 0$ if and only if $x = y$ ;
(APM3): $P (x, y, t) = P (y, x, t) \forall x, y \in X, \forall t \in (0, + \infty)$ ;
(APM4): $P (x, z, t + s) \leq P (x, y, t) + P (y, z, s) \forall x, y, z \in X, \forall t \in (0, + \infty)$ ;
(APM5): $P (x, y, -) : (0, \infty) \to [0, 1]$ is left semicontinuous for all $x, y \in X .$

Patterned after Definition 5 we introduce the strong version of the approximating parametrized metric.

Definition 7.

An approximating parametrized metric P on a set X is called strong if in addition to axioms(APM1)-(APM3)the following modifications of axioms(APM4)and(APM5)are satisfied

(APM4): $P (x, z, t) \leq P (x, y, t) + P (y, z, t)$ for all $x, y, z \in X$ and for all $t \in (0, \infty)$ .
(APM5): $P (x, y, -) : R^{+} \to [0, 1]$ is left continuous and non-increasing, (that is $t < s ⟹ P (x, y, t) \leq P (x, y, s) \forall x, y \in X .$ ).

It turns out that under certain conditions, fuzzy approximating metrics and approximating parametrized metrics can be considered dual concepts. The details of this statement will be revealed later in this section.

Theorem 1.

Let

M : X \times X \times (0, \infty) \to [0, 1]

be a fuzzy approximating metric for the Hamacher t-norm and let

P_{M} (x, y, t) = \frac{1 - M (x, y, t)}{M (x, y, t)} .

Then

P_{M} : X \times X \times (0, + \infty) \to R^{+}

is an approximating parametrized metric.

Proof Note first that since

M (x, y, t) > 0

for every

x, y \in X

t \in (0, + \infty)

the definition of

P_{M}

is correct. Besides, from Proposition 1 it follows that the function

P_{M} : X \times X \times (0, + \infty) \to R^{+}

is non-increasing.

Property (APM1) for

P_{M}

follows directly from Property (FAM1) for

M : X \times X \times (0, \infty) \to [0, 1]

Referring to axiom (FAM2) and recalling that by Proposition 1, the limit

{lim}_{t \to \infty} M (x, y, t) =_{d e f} a \in [0, 1]

exists for every pair

x, y \in X,

we establish axiom (APM2) as follows:

{lim}_{t \to \infty} P_{M} (x, y, t) = {lim}_{t \to \infty} \frac{1 - M (x, y, t)}{M (x, y, t)} = \frac{1 - {lim}_{t \to \infty} M (x, y, t)}{{lim}_{t \to \infty} M (x, y, t)} = 0

if and only if

{lim}_{x \to \infty} M (x, y, t) = 1

, i.e. if and only if

x = y

Referring to axiom (FAM3) we establish property (APM3) for

P_{M} : X \times X \times (0, + \infty) \to R^{+}

as follows:

P_{M} (x, y, t) = \frac{1 - M (x, y, t)}{M (x, y, t)} = \frac{1 - M (y, x, t)}{M (y, x, t)} = P_{M} (y, x, t) \forall x, y \in X, \forall t \in (0, + \infty)

To show (APM5) notice that by (FAM5)

{lim}_{t ↗ t_{0}} P_{M} (x, y, t) = {lim}_{t ↗ t_{0}} \frac{1 - M (x, y, t)}{M (x, y, t)} = \frac{1 - {lim}_{t ↗ t_{0}} M (x, y, t)}{{lim}_{t ↗ t_{0}} M (x, y, t)} = \frac{1 - M (x, y, t_{0})}{M (x, y, t_{0})} = P_{M} (x, y, t_{0})

and recall that

P_{M} (x, y, -)

is non-increasing.

To complete the proof we have to show that axiom (FAM4) in case of Hamacher t-norm for

M : X \times X \times (0, + \infty) \to [0, 1]

, i.e.

\frac{M (x, y, t) \cdot M (y, z, s)}{M (x, y, t) + M (y, z, s) - M (x, y, t) \cdot M (y, z, s)} \geq M (x, z, t + s) \forall x, y, z \in X, t, s \in (0, \infty)

implies property (APM4) for

P_{M} : X \times X \times (0, + \infty) \to R^{+}

, i.e.

P_{M} (x, y, t) + P_{M} (y, z, s) = \frac{1 - M (x, y, t)}{M (x, y, t)} + \frac{1 - M (y, z, s)}{M (y, z, s)} \geq

\frac{1 - M (x, z, t + s)}{M (x, z, t + s)} = P_{M} (x, z, t + s) \forall x, y, z \in X, \forall t, s \in (0, \infty)

We denote

M (x, y, t) = a, M (y, z, s) = b, M (x, z, t + s) = c

. We have to prove that

\frac{1 - c}{c} \leq \frac{1 - a}{a} + \frac{1 - b}{b}

under assumption that

(⋄) \frac{a \cdot b}{a + b - a \cdot b} \leq c

Now we prove the requested inequality as follows:

\frac{1 - c}{c} \leq \frac{1 - \frac{a \cdot b}{a + b - a \cdot b}}{\frac{a \cdot b}{a + b - a \cdot b}} = \frac{a + b - 2 \cdot a \cdot b}{a \cdot b} = \frac{a - a \cdot b}{a \cdot b} + \frac{b - a \cdot b}{a \cdot b} = \frac{1 - b}{b} + \frac{1 - a}{a} .

□

In the proof of the property (APM4) the crucial role was played by the inequality

(⋄)

where the left side of the inequality was the result obtained by the Hamacher t-norm. Noticing this, we can get the following corollary from the previous theorem.

Corollary 1.

Let

M : X \times X \times (0, \infty) \to [0, 1]

be a fuzzy approximating metric for a t-norm which is weaker (i.e. smaller) or equal to the Hamacher t-norm. Then by setting

P_{M} (x, y, t) = \frac{1 - M (x, y, t)}{M (x, y, t)} .

we get an approximating parametrized metric

P_{M} : X \times X \times (0, + \infty) \to R^{+}

. In particular if it is a fuzzy metric for the product and ukasiewicz t-norm.

Theorem 2.

Let

P : X \times X \times (0, + \infty) \to R^{+}

be an approximating parametrized metric on a set X. Then the mapping

M_{P} : X \times X \times (0, \infty) \to [0, 1]

defined by

M_{P} (x, y, t) = \frac{1}{1 + P (x, y, t)}, \forall x, y \in X, t \in (0, \infty)

is a fuzzy approximating metric for the Hamacher t-norm.

Proof We can verify that each one of axioms (APM1) - (APM5) for P implies the validity of the corresponding condition (FAM1) - (FAM5), for the mapping P similarly, as we did in the opposite direction in the proof of Theorem 1. We shall linger only on the proof of less trivial property

(F A M 4)

in case * is the Hamacher t-norm. Explicitly, we have to prove that for all

x, y, z \in X

and for all

t, s \in (0, \infty)

the following inequality holds:

M_{P} (x, z, t + s) \geq \frac{M_{P} (x, y, t) \cdot M_{P} (y, z, s)}{M_{P} (x, y, t) + M_{P} (y, z, s) - M_{P} (x, y, t) \cdot M_{P} (y, z, s)}

under assumption that

P (x, z, t + s) \leq P (x, y, t) + P (y, z, s) \forall x, y, z \in X, \forall t, s \in (0, \infty)

We fix

x, y, z \in X, t, s \in (0, \infty)

, denote

P (x, z, t + s) = c, P (x, y, t) = a, P (y, z, s) = b

and referring to the definition of

M_{P}

through P, we rewrite the provable inequality by

\frac{1}{1 + c} \geq \frac{\frac{1}{1 + a} \cdot \frac{1}{1 + b}}{\frac{1}{1 + a} + \frac{1}{1 + b} - \frac{1}{1 + a} \cdot \frac{1}{1 + b}}

which, after elementary transformation comes to the accepted inequality

c \leq a + b

Corollary 2.

Let

P : X \times X \times (0, \infty) \to [0, \infty)

be an approximating parametrized metric and define

M_{P} : X \times X \times (0, \infty) \to [0, 1]

M_{P} (x, y, t) = \frac{P (x, y, t)}{1 + P (x, y, t)} \forall x, y \in X, t \in (0, \infty) .

Then

M_{P}

is a fuzzy approximating metric for any t-norm * such that

* \leq *_{H}

. In particular, it is a fuzzy metric for the product and ukasiewicz t-norms.

Notice that from the above constructions and from Theorem 1 and Theorem 2 we have the following corollaries:

Corollary 3.

For every approximating parametrized metric M the equality

P_{M_{P}} = P

holds.

Corollary 4.

1 If

M : X \times X \times (0, \infty) \to [0, 1]

is a fuzzy metric for a t-norm * such that

* \leq *_{H}

then

M = M_{P_{M}}

Remark 4.

Note however, that when writing

M_{P_{M}} = M

, the equality “forgets" the original t-norm * used in the definition of the fuzzy metric (and hence such that

* \leq *_{H}

by Corollary 1) and the resulting fuzzy metric

M_{P_{M}}

(by Theorem 1) is a fuzzy metric for the Hamacher t-norm

*_{H}

Corollary 5.

Approximating parametrized metrics and fuzzy approximating metrics for Hamacher t-norms are equivalent concepts.

One can easily modify the proofs of the Theorems 1 and 2 and their corollaries for the case of strong fuzzy approximating metrics and strong approximating parametrized metrics. Namely, the following statements hold:

Theorem 3.

Let

M : X \times X \times (0, \infty) \to [0, 1]

be a strong fuzzy metric for the Hamacher t-norm and let

P_{M} (x, y, t) = \frac{1 - M (x, y, t)}{M (x, y, t)} .

Then

P_{M} : X \times X \times (0, + \infty) \to R^{+}

is a strong approximating parametrized metric.

Theorem 4.

Let

P : X \times X \times (0, + \infty) \to R^{+}

be a strong approximating parametrized metric on a set X. Then the mapping

M_{P} : X \times X \times (0, \infty) \to [0, 1]

defined by

M_{P} (x, y, t)) = \frac{1}{1 + P (x, y, t)}, \forall x, y \in X, t \in (0, \infty)

is a strong fuzzy approximating metric for the Hamacher t-norm.

Corollary 6.

Strong approximating parametrized metrics and strong fuzzy approximating metrics for Hamacher t-norms are equivalent concepts.

Patterned after the definition of an ultrametric, a mapping

M : X \times X \times (0, \infty) \to [0, 1]

satisfying properties (APM1), (APM2), (APM3), (APM5) of the Definition 6 and the following stronger version of the property (APM4)

(APM 4^{u}) P (x, z, t + s) \leq P (x, y, t) \land P (y, z, s) \forall x, y, z \in X, \forall t, s \in (0, + \infty)

will be called an approximating parametrized ultrametric .

The following theorem shows that approximating parametrized ultrametrics correspond to fuzzy approximating metrics for the minimum t-norm.

Theorem 5.

M : X \times X \times (0, + \infty) \to [0, 1]

is a fuzzy approximating metric for the minimum t norm, then by setting

P_{M} (x, y, t) = \frac{1 - M (x, y, t)}{M (x, y, t)}

we get an approximating parametrized ultrametric

P : X \times X \times (0, + \infty) \to R^{+}

. Conversely, given an approximating parametrized metric

P : X \times X \times (0, + \infty) \to R^{+}

, by setting

M_{P} (x, y, t) = \frac{1}{1 + P (x, y, t)}

we obtain a fuzzy approximating metric for the minimum t-norm.

Proof Obviously we have to prove that condition

(APM 4^{u})

for a

P : X \times X \times (0, + \infty) \to R^{+}

is equivalent to axiom (FAM4) in case to compare them with of the minimum t-norm. In other way stated, by using notations

M (x, y, t) = a

M (y, z, t) = b

M (x, z, t)

we have to prove that

a \lor b \geq c ⟹ \frac{1}{1 + c} \geq \frac{1}{1 + a} \land \frac{1}{1 + b} ⟹ \frac{1 - a}{a} \land \frac{1 - b}{b} \leq \frac{1 - c}{c}

for

a, b, c \in (0, 1]

. However, to see this just assume that

a \leq b

. □.

4. Fuzzy approximating metrics versus fuzzy partial metrics

Partial metrics were introduced in 1994 by Matthews [20] and now they are in the focus of interest for some mathematicians and theoretical computer scientists, see, e.g. the survey [5].

Basing on the concept of a partial metric, V. Gregori, J-J. Minana and D. Miravet [16] introduced the concept of a fuzzy partial metric, both in KM and GV versions. We rely here on the GV-version of a fuzzy partial metric to compare them with our fuzzy approximating metrics.

Definition 8.

[16] Let X be a set, * a continuous t-norm and

\mapsto_{*} : [0, 1] \times [0, 1] \to [0, 1]

the corresponding residuum. A fuzzy partial metric on a set X is a mapping

p : X \times X \times R^{+} \to [0, 1]

satisfying the following conditions for all

x, y, z \in X

and all

s, t > 0

(FPM0): $p (x, y, t) > 0;$
(FPM1): $p (x, x, t) \geq P (x, y, t);$
(FPM2): $p (x, x, t) = P (x, y, t) = P (y, y, t)$ if and only if $x = y;$
(FPM3): $p (x, y, t) = p (y, x, t);$
(FPM4): $p (x, x, t) \mapsto_{*} p (x, z, t + s) \geq (p (x, x, t) \mapsto_{*} p (x, y, t)) * (p (y, y, s) \mapsto_{*} p (y, z, s));$
(FPM5): mapping $p (x, y, -) : (0, \infty) \to [0, 1]$ is a lower semicontinuous function.

One can easily notice certain common features between our fuzzy approximating metrics on one side and partial and fuzzy partial metrics on the other. And this is not surprise, since the idea of both approaches is the problem of evaluation of the distance between two infinite strings. Namely, in practice the result will not be obtained as given or received at some step, but will be achieved in the infinite process of comparing of these strings. However, the tools suggested for this research, that is fuzzy approximating metrics and (fuzzy) partial metrics are essentially different. In this section we make a preliminary comparison of fuzzy approximating metrics and fuzzy partial metrics.

We make this comparison by comparing individual axioms in the definitions in case when x and y are infinite strings. Note first that conditions (FPM0), (FPM1), (FPM3) and (FPM5) are equivalent to conditions (FAM0), (FAM1), (FAM3) and (FAM5) respectively. So, we have to compare (FPM2) with (FAM2) and to compare (FPM4) with (FAM4).

Speaking informally, condition (FAM2) means that, if comparing strings x and y, we notice that they coincide at each step

{⌊ t ⌋}^{t h}

and hence

{lim}_{t \to \infty} M (x, y, t) = 1

, we conclude that

x = y

. On the other hand, condition (FPM2) asks to compare values

p (x, x, t)

p (x, y, t)

and

p (y, y, t)

at each step

⌊ t ⌋

and if they are equal, then conclude that

x = y

. In this case no information about the value

p (x, x, t)

and specifically of its limit at the infinity for a given

x \in X

is specified. So, axioms (FPM2) and (FAM2) are incomparable. although (FPM2) seems us more flexible than (FAM2).

In order to compare (FPM4) with (FAM4), we have to fix a t-norm, since they are the only axioms where t-norm (and hence the corresponding residuum

\mapsto_{*}

) is involved. We restrict here to the case of ukasiewicz t-norm and the case of the minimum t-norm and the strong version of the fuzzy approximating version. Just in these cases the comparison of the axioms becomes most visual.

In case of the ukasiewicz t-norm condition (FPM4) can be rewritten as

p (x, z, t + s) \geq p (x, y, t) + p (y, z, s) - p (y, y, t + s) f o r e a c h t, s > 0

and hence if

{lim}_{t \to \infty} p (x, x, t) = 1

for each

x \in X

we have

{lim}_{t, s \to \infty} p (x, z, t + s) \geq {lim}_{t \to \infty} p (x, y, t) + {lim}_{t \to \infty} p (y, z, s) - 1 .

On the other hand the condition (FAM4) can be rewritten as

M (x, z, t + s) \geq M (x, y, t) + M (y, z, s) - 1 f o r e v e r y t > 0 .

So although concepts of a fuzzy partial metric and fuzzy approximating metric are independent, the concept of a fuzzy partial metric in case of ukasiewicz t-norm seems more flexible than the concept of a fuzzy approximating metric.

Now in case of the minimum t-norm and taking into account axiom (FPM1) the “strong version”

(F P M 4^{s}) p (x, x, t) \mapsto_{*} p (x, z, t) \geq (p (x, x, t) \mapsto_{*} p (x, y, t)) * (p (y, y, t) \mapsto_{*} p (y, z, t))

of the axiom

(F P M 4)

can be rewritten as

p (x, z, t) \geq p (x, y, t) \land p (y, z, t)

and this is just the axiom

(F A M 4^{s})

of the strong fuzzy approximating metric

M : X \times X \times (0, \infty) \to [0, 1] .

5. Examples of application of the constructed approximating parametrized metric in word combinatorics

In our previous papers we have presented several approaches and constructions of fuzzy metrics [2,24], parametrized metrics [4] and fuzzy approximating metrics [3], which describe distance between any infinite words. In these papers we stress inappropriate numerical results for ordinary metrics on the universe of the infinite words. Here, for the evaluation of the distance between infinite words, we will use a strong approximating parametrized metric constructed by means Theorem 1 from a strong fuzzy approximating metric considered in [3] .

Let X be the set of infinite words. We define a sequence

{d_{n} ∣ n \in N \cup {0}}

of metrics on X as follows. Let

x = (x_{0}, x_{1}, x_{2}, \dots), y = (y_{0}, y_{1}, y_{2}, \dots) \in X

and let

χ_{i} (x, y) = 0 i f x_{i} = y_{i}

and

χ_{i} (x, y) = 1 i f x_{i} \neq y_{i}

. We define a sequence of metrics:

{\{d_{n} (x, y) = \sum_{i = 0}^{n} (\frac{5}{6 + i} + \frac{2}{3}) χ_{i} (x, y)\}}_{n \in N \cup {0}} .

Basing on this sequence of metrics we construct the sequence of strong fuzzy approximating metrics in case of the Drastic t-norm

T_{D}

on the set X of all infinite words:

{\{μ_{n} (x, y, t) = \frac{t - d_{n} (x, y)}{t + c} \lor 0\}}_{n \in N \cup {0}}

, where

c \in R_{+}

Further, we define the following family of mappings:

{\{M_{n} (x, y, t) = M_{n - 1} (x, y, n) \lor μ_{n} (x, y, t)\}}_{n \in N \cup {0}} .

Finally, we construct a mapping

M : X \times X \times R^{+} \to (0, 1]

as follows:

M^{c} (x, y, t) = \{\begin{matrix} M_{0} (x, y, t) & i f 0 < t \leq 1 \\ M_{1} (x, y, t) & i f 1 < t \leq 2 \\ M_{2} (x, y, t) & i f 2 < t \leq 3 \\ \dots \\ M_{n} (x, y, t) & i f n < t \leq n + 1 \\ \dots \end{matrix}

It is provable that the mapping

M^{c} : \times X \times R^{+} \to [0, 1]

is a strong fuzzy approximating pseudometric in case of the drastic t-norm

T_{D}

(for details, see [3]). Now from Theorem 1 we define an approximating parametrized metric

P_{M^{c}} (x, y, t) = \frac{1 - M^{c} (x, y, t)}{M^{c} (x, y, t)}

and define

{lim}_{t \to \infty} P_{M^{c}} (x, y, t) = Γ (x, y) .

Suppose we compare three infinite words

x = (1^{m} 000 \dots)

y = (0^{m} 111 \dots)

and

z = (0000 \dots),

where

m \in N

and the symbol

0^{m}

defines a string of the length m, which consists only of zeroes (similarly

1^{m}

a string of ones). For a finite m we expect that

Γ (x, z) < Γ (y, z)

as infinite words x and z differ only at finite positions (the first m) but coincide at all other positions. We introduce the numerical results of this approximating parametrized metric by choosing different parameters c and we also search for the change point i.e. the length m of the prefix, where the sign for inequality

Γ (x, z) < Γ (y, z)

changes. We start with the parameter

c = 1

As we can see in Table 1, for our approximating parametrized metric

P_{M^{c}}

we get the expected value for

m \leq 370

(although the change point for our construction of approximating parametrized metric

P_{M^{c}}

is not small, i.e., at

m = 370

, this result means that already first 371 positions are more important than all other infinite ones). The one number which outshines from the Table 1 is

Γ (y, z) = 1

, if

m = 1

. As y and z coincide only in the first positions and differ in all others, then approximating parametrized metric gives value

M^{c} (y, z, t) = 0.5

as the first position have the weight one half. From this we get that

Γ (y, z) = {lim}_{t \to \infty} P_{M^{c}} (x, y, t) = {lim}_{t \to \infty} \frac{1 - M^{c} (y, z, t)}{M^{c} (y, z, t)} = \frac{1 - 0.5}{0.5} = 1 .

Increase of the parameter c gives us a possibility to put smaller weights on the prefix. For small m we get the expected inequality

Γ (x, z) < Γ (y, z)

, but at some point we reach the change point (see Table 2 and Table 3). Of course it can be fixed by increasing the parameter c unlimitedly.

Another important point that can be seen in both Table 2 and Table 3 is that

Γ (y, z) \approx 2 > 1

for

m = 10

and

m = 100

P_{M^{c}} : X \times X \times (0, + \infty) \to R^{+}

and

{lim}_{t \to \infty} M^{c} (y, z, t) \approx \frac{1}{3}

{lim}_{t \to \infty} P_{M^{c}} (y, z, t) \approx 2

In this case, if we increase the parameter c unlimitedly, then for

m = 1

the value

P_{M^{c}}

tends to ∞.

6. Conclusions

We have introduced the concept of a fuzzy approximating metric, investigated close relations, depend on the choice of a t-norm, between fuzzy approximating metrics and approximating parametrized metrics, discussed relations between fuzzy approximating metrics and fuzzy partial metrics and illustrated some possible applications of approximating parametrized metrics in the problems of words combinatorics.

We foresee several directions, both theoretical and practical ones, in which the research started in this work can be continued.

As the first step, we see the practical use of fuzzy approximating metrics in the problems of words combinatorics. In particular, to study the advantages/disadvantages of approximating parametrized metrics if compared with other metric type structures, in particular, those ones which were used in [4], by analysing specific numerical examples.
As the second important issue to be studied is the topological structure induced by fuzzy approximating metrics. A non-triviality of this problem is caused by the fact that the “open balls” induced by fuzzy approximating metrics need not open in the topological sense (as it is in the situation of b-metric spaces, see e.g. [11,12]) and this leads to different possible approaches to the study of topology-related issues.
We also plan to study fuzzy approximating metric spaces as categories. In particular, to define in the appropriate way continuity of mappings of fuzzy approximating spaces, to investigate their products, coproducts and other operations.
A challenging issue would be to carry out a deeper comparative analysis (in particular from categorical point of view) between fuzzy approximating metrics, approximating parametrized metrics and fuzzy partial metrics.

Author Contributions

Both authors contributed equaly to this paper.

Funding

The work was supported by ERDF within the project No.1.1.1.2/16/I/001, application No.1.1.1.2/VIAA/4/20/706 “Applications of Fuzzy Pseudometrics in Combinatorics on Words”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The work has been supported by ERDF within the project No.1.1.1.2/16/I/001, application No.1.1.1.2/VIAA/4/20/706 “Applications of Fuzzy Pseudometrics in Combinatorics on Words”.

Conflicts of Interest

The authors declare no conflict of interest.

Sample Availability

Not applicable.

References

Afrouzi, G. A., Shakeri, S., Rasouli, S. H. On the fuzzy metric spaces, Journal Mathematics and Computer Science. 2 (3) (2011) 475-482.
Bēts, R., Šostak, A. Fragmentary Fuzzy Pseudometrics: Basics of the Theory and Applications in Combinatorics on Words. Baltic J. Modern Computing, 4 (2016), 826–845.
Bēts, R., Šostak, A. Some remarks on strong fuzzy metrics and strong fuzzy approximating metrics with applications in word combinatorics, MATHEMATICS 10 (2022) art. 738.
Bēts, R., Šostak, A., Miķelsons, E.M. Parametrized metrics and their applications in word combinatorics Proc. IPMU2022 (to appear).
Bukatin M., Kopperman R., Matthews S., Pajoohesh H. Partial Metric Spaces American Math. Monthly 116 (2009), 708–718.
Calude, C. S., Jürgensen, H., Staiger, L. Topology on words. Theoretical Computter Science, 410, (2009), 2323–2335.
McClendon, J. F.: Metric families, Pacific J. Mathematics. 57 (2), 491–509 (1975).
N. Hussain, S. Khaleghizadeh, P. Salimi, Afhar A.N. Abdou, A new approach to fixed point results in triangular intuitionistic fuzzy metric spaces, Abstract and Applied Analysis. 2014 (2014) art. Id 690139.
Radu, V.: Families of metrics for distribution functions, Proceedings 7th Conference on Probablity Theory Brasov, Romania. 487-492 (1982).
Schueth, D.: Continuous families of isospherical metrics on simply connected manifolds, Annals of Mathematics, Second Series, 149, 287-308 (1999).
R. Saadati, On the topology of fuzzy metric type spaces Filomat 29:1 (2015), 133-141.
A. Šostak Some remarks on fuzzy k-pseudometric spaces, FILOMAT 32:10, 3567-3580 (2017).
George, A., Veeramani, P. (1994). On some results in fuzzy metric spaces, Fuzzy Sets Syst., 64, 395–399.
George, A., Veeramani, P. (1997). On some results of analysis for fuzzy metric spaces, Fuzzy Sets Syst., 90, 365–368.
Grecova, S., Šostak, A., Uļjane. I. (2016) A construction of a fuzzy topology from a strong fuzzy metric A construction of a fuzzy topology from a strong fuzzy metric, Applied General Topology, 17(2), 106–115.
Gregori, V. , Miñana, D. Miravet Fuzzy partial metric spaces, Internat. J. General Systems, 48 (2019), 260–271.
Klement, E. P., Mesiar, R., Pap, E. Triangular norms, Kluwer Acad. Publ. 2000.
Kopperman, R. , Matthews, S., Pajoohesh, H., Partial metrizability in value quantales, Applied General Topology 5 (2004), 115–127.
Kramosil, I., Michalek, J. Fuzzy metrics and statistical metric spaces, Kybernetika 11, (1975), 336 – 344.
S. G. Matthews, Partial Metric Topology, Proc. 8th Summer Conf. Topology and Applications, vol. 728, Annals of The New york Academy of Sciences, (1994), 183–197.
Menger, K. . Probabilistic geometry, Proc. N.A.S. 27, (1951), 226–229.
A. Sapena, S. A. Sapena, S. Morillas, On strong fuzzy metrics, Proc. Workshop Appl. Topology WiAT’09 (2009), 135-141.
Schweizer, B., Sklar, A. . Statisitcal metric spaces, Pacific J. Math. 10, (1960), 215–229.
Šostak, A. , Bēts, R. (2017). Fuzzy φ-pseudometrics and Fuzzy φ-pseudometric Spaces. EUSFLAT/IWIFSGN (3) (2017), 328-340.
Šostak, A., Bēts, R., Grigorenko, O. Two kinds of parametrized metrics: construction, topological properties and applications. Topology and Appl. 330 (2023).

1	In the original definition in [13] $M (x, y, -) : (0, \infty) \to [0, 1]$ was assumed to be continuous.

Table 1. The values of approximating parametrized metric for parameter

c = 1

and various m

Table 1. The values of approximating parametrized metric for parameter

c = 1

and various m

m	1	370	371	50000
$Γ (x, z)$	0.00003	0.00269	0.0027	0.5010
$Γ (y, z)$	1	0.0027	0.00269	0.00002

Table 2. The values of the approximating parametrized metric for

c = 10

and various m

Table 2. The values of the approximating parametrized metric for

c = 10

and various m

m	1	1192	1193	50000
$Γ (x, z)$	0.00011	0.00838	0.00839	0.5012
$Γ (y, z)$	2.00458	0.00839	0.00838	0.0002

Table 3. The values of the approximating parametrized metric for

c = 100

and various m

Table 3. The values of the approximating parametrized metric for

c = 100

and various m

m	1	3725	3726	50000
$Γ (x, z)$	0.00102	0.02683	0.02684	0.8001
$Γ (y, z)$	2.00729	0.02684	0.02683	0.002

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Fuzzy Approximating Metrics, Approximating Parametrized Metrics and Their Relations With Fuzzy Partial Metrics

Abstract

0. Introduction

1. Preliminaries: Fuzzy metrics

2. Fuzzy approximating metrics

3. Approximating parametrized metrics

4. Fuzzy approximating metrics versus fuzzy partial metrics

5. Examples of application of the constructed approximating parametrized metric in word combinatorics

6. Conclusions

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Acknowledgments

Conflicts of Interest

Sample Availability

References

MDPI Initiatives

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