Book ChapterDOI

# Comparison of Different Algorithms of Approximation by Extensional Fuzzy Subsets

01 Jan 2013-Advances in intelligent systems and computing (Springer, Berlin, Heidelberg)-pp 307-317

TL;DR: Different methods to approximate an arbitrary fuzzy subset by an adequate extensional one are compared in order to understand better the performance and improvement they give.
Abstract: How to approximate an arbitrary fuzzy subset by an adequate extensional one is a key question within the theory of Extensional Fuzzy Subsets. In a recent paper by the authors  different methods were provided to find good approximations. In this work these methods are compared in order to understand better the performance and improvement they give.
Topics: Fuzzy logic (56%)

Content maybe subject to copyright    Report Comparison of Different Algorithms of
Approximation by Extensional Fuzzy Subsets
Gabriel Mattioli and Jordi Recasens
Abstract How to approximate an arbitrary fuzzy subset by an adequate extensional
one is a key question within the theory of Extensional Fuzzy Subsets. In a recent
paper by the authors  different methods were provided to ﬁnd good approxi-
mations. In this work these methods are compared in order to understand better the
performance and improvement they give.
1 Introduction
Indistinguishability operators were introduced to fuzzify the concept of crisp equiv-
alence relations. These operators allow to model the idea of ”similarity” between
elements, which is key to understand how we ”identify” objects. The operation of
identiﬁcation is the mainstone to simplify the representation we have of the en-
vironment and understand the information given by our perception. Being able to
identify objects enables us to store less quantity of information if favour of being
able to extract a qualitative analysis of it.
An eye without a mechanism to identify objects is nothing but a sensor of outern
reality. An eye with this mechanism becomes a perceptive system that can ”under-
stand” the environment.
Under an indistinguishability operator the observable fuzzy sets are the exten-
sional ones. These sets correspond to the fuzziﬁcation of classical equivalence
classes. Within the theory of Fuzzy Logic the ﬁrst researcher to point the relevance
of these sets was Zadeh when he discussed the concept of granularity .
Department of Mathematics and Computer Science
ETS Arquitectura del Valles
Universitat Polit`ecnica de Catalunya
C/ Pere Serra 1-15. 08190 Sant Cugat del Valls. Spain
e-mail: gabriel.mattioli@upc.edu
e-mail: j.recasens@upc.edu
1 2 Gabriel Mattioli and Jordi Recasens
If we assume that indistinguishability operators are a good model to understand
similarities between objects (and there is evidence to think so), then a very interest-
ing problem is how an arbitrary fuzzy subset can be approximated by an extensional
one with the minimum loss of accuracy.
In a previous work by the authors  this problem was faced and 3 methods
were derived for Archimedean t-norms (T = Łukasievicz and T =
Π
product) and
one for the Minimum t-norm.
Restricting to Archimedean t-norms, the ﬁrst method was based on ﬁnding an
adequate mean between two operators that provide the best upper and lower ap-
proximation by extensional fuzzy subsets of a given fuzzy subset
µ
. The second
one computed an adequate power of the lower approximation of
µ
, and the last one
found the solution solving a Quadratic Programming problem.
Big differences can be found between the ﬁrst two and the last method. The QP-
based one guarantees that the solution found is optimal while the ﬁrst two do not.
On the other hand, the last method suffers drastically the curse of dimensionality
and becomes computationally unaffordable for large cardinalities of the universe
of discourse X. The ﬁrst two do not have this problem and work even when X is
non-ﬁnite.
The aim of this work is to compare in depth the mean-based and the power-based
methods. In order to reduce the scope of this comparison we will restrict to the
Łukasievicz t-norm and to ﬁnite sets. This has been done because in  explicit
formulas were provided to ﬁnd the best approximations when T = Ł, while the best
approximation for T =
Π
had to be found by numerical methods.
The work is structured as follows:
In Section 2 the Preliminaries to this work are given. In this section the deﬁnition
and main properties of indistinguishability operators and extensional sets will be
recalled.
Section 3 will show how the mean-based method can be built. Natural weighted
means will be introduced ﬁrst and explicit formulas will be provided to ﬁnd the
extensional fuzzy subset that better approximates
µ
following this method.
In Section 4 the power-based method will be given. First of all it will be shown
how powers can be deﬁned with respect to a t-norm T and further how this can be
used to ﬁnd good approximations by extensional fuzzy subsets.
In Section 5 a comparison between these two methods will be provided. Fixed an
indistinguishability operator E we will study the output and error committed by each
of the methods when approximating different fuzzy subsets and some conclusions
will be extracted.
Finally, the Concluding Remarks of this work will be given in Section 6. Comparison of Different Algorithms of Approximation by Extensional Fuzzy Subsets 3
2 Preliminaries
In this section the main concepts and results used in this work will be given. The
deﬁnition of indistinguishability operator will be recalled as well as the main prop-
erties of the extensional fuzzy subsets related to an indistinguishability operator.
First of all let us recall the well known Ling’s Theorem which introduces the
concept of additive generator t of a continuous Archimedean t-norm. Additive gen-
erators will prove to be very useful further in this work.
Theorem 1  A continuous t-norm T is Archimedean if and only if there exists a
continuous and strictly decreasing function t : [0,1] [0, ] with t(1) = 0 such that
T(x,y) = t

(t(x) + t(y))
where t

is the pseudo inverse of t deﬁned by
t

(x) =
1 if x 0
t
1
(x) if 0 x t(0)
0 if t(0) x.
The function t will be called an additive generator of the t-norm and two generators
of the t-norm T differ only by a positive multiplicative constant.
If T = Ł is the Łukasievicz t-norm, then an additive generator is t(x) = 1 x.
If T =
Π
is the Product t-norm, then t(x) = log(x).
Deﬁnition 1 Let T be a t-norm.
The residuation
T of T is deﬁned for all x,y [0,1] by
T (x|y) = sup{
α
[0,1]|T(
α
,x) y}.
The birresiduation
T of T is deﬁned for all x,y [0,1] by
T (x,y) = min{
T (x|y),
T (y|x)} = T(
T (x|y),
T (y|x)).
When the t-norm T is continuous Archimedean, these operations can be rewritten
in terms of the additive generator t.
Proposition 1 Let T be a continuous Archimedean t-norm generated by an additive
generator t. Then:
T(x,y) = t

(t(x) + t(y))
T (x|y) = t

(t(y) t(x))
T (x,y) = t

(|t(x) t(y)|). 4 Gabriel Mattioli and Jordi Recasens
Indistinguishability operators are the fuzziﬁcation of classical equivalence re-
lations and model the intuitive idea of ”similarity” between objects. For a more
detailed explanation on this operators readers are referred to , .
Deﬁnition 2 Let T be a t-norm. A fuzzy relation E on a set X is a T-indistinguish-
ability operator if and only if for all x,y,z X
a) E(x,x) = 1 (Reﬂexivity)
b) E(x,y) = E(y,x) (Symmetry)
c) T(E(x, y),E(y,z)) E(x,z) (T -transitivity).
Whereas indistinguishability operators represent the fuzziﬁcation of equivalence
relations, extensional fuzzy subsets play the role of fuzzy equivalence classes al-
together with their intersections and unions. Extensional fuzzy subsets are a key
concept in the comprehension of the universe of discourse X under the effect of
an indistinguishability operator E as they correspond with the observable sets or
granules of X.
Deﬁnition 3 Let X be a set and E a T-indistinguishability operator on X. A fuzzy
subset
µ
of X is called extensional with respect to E if and only if:
x,y X T(E(x,y),
µ
(y))
µ
(x).
We will denote H
E
the set of all extensional fuzzy subsets of X with respect to E.
Extensional fuzzy subsets have been widely studied in the literature , ,
.
If the t-norm T is continuous Archimedean then the condition of extensionality
can be rewritten in terms of additive generators. This result will be recalled several
times along this paper.
Lemma 1 Let E be a T -indistinguishability operator on a set X.
µ
H
E
if and only
if x,y X:
t(E(x,y)) + t(
µ
(y)) t(
µ
(x)).
Proof.
µ
H
E
T(E(x,y),
µ
(y))
µ
(x)
t
1
(t(E(x,y)) + t(
µ
(y)))
µ
(x).
And as t is a monotone decreasing function this is equivalent to
t(E(x,y)) + t(
µ
(y)) t(
µ
(x)).
3 Approximation using Means
In this section we will propose a method to approximate an arbitrary fuzzy subset
by an extensional one. First we will introduce two approximation operators,
φ
E
(
µ
) Comparison of Different Algorithms of Approximation by Extensional Fuzzy Subsets 5
and
ψ
E
(
µ
), that provide the best upper and lower approximation respectively by
extensional fuzzy subsets of
µ
given an indistinguishability operator E. The method
will consist in computing an adequate weight in order to minimize an error function
between
µ
and the natural weighted mean of
φ
E
(
µ
) and
ψ
E
(
µ
).
Deﬁnition 4 Let X be a set and E a T-indistinguishability operator on X. The maps
φ
E
: [0,1]
X
[0,1]
X
and
ψ
E
: [0,1]
X
[0,1]
X
are deﬁned x X by:
φ
E
(
µ
)(x) = sup
yX
T(E(x,y),
µ
(y)),
ψ
E
(
µ
)(x) = inf
yX
T (E(x, y)|
µ
(y)).
φ
E
(
µ
) is the smallest extensional fuzzy subset greater than or equal to
µ
; hence
it is its best upper approximation by extensional fuzzy subsets. Analogously,
ψ
E
(
µ
)
provides the best approximation by extensional fuzzy subsets smaller than or equal
to
µ
. From a topological viewpoint these operators can be seen as closure and inte-
rior operators on the set [0,1]
X
. It is remarkable that these operators also appear
in a natural way in ﬁelds such as fuzzy rough sets , fuzzy modal logic , ,
fuzzy mathematical morphology  and fuzzy contexts  among many others.
Though
φ
E
(
µ
) and
ψ
E
(
µ
) provide extensional fuzzy subsets that approximate
µ
there is no guarantee in general that there are no better approximations of
µ
by
extensional fuzzy subsets. In  the authors faced this problem and provided three
methods to ﬁnd approximations for Archimedean t-norms and one for the Minimum
t-norm. The two methods compared in this paper were introduced there.
Deﬁnition 5  Let t : [0,1] [,] be a non-increasing monotonic map, x,y
[0,1] and r [0,1]. The weighted quasi-arithmetic mean m
t
of x and y is deﬁned as:
m
r
t
(x,y) = t
1
(r·t(x) + (1 r) · t(y))
m
t
is continuous if and only if {−,} * Ran(t).
There is a bijection between the set of continuous Archimedean t-norms and
the set of quasi-arithmetic means by taking as map the additive generator t of the
t-norm . Under this interpretation in the literature quasi-arithmetic means are
sometimes called natural means , as we will recall them from now on.
We want to approximate
µ
by m
r
t
(
φ
E
(
µ
),
ψ
E
(
µ
)). Below we prove that this mean
is extensional for any value of r.
Proposition 2  Let X be a set and
µ
,
ν
extensional fuzzy subsets of X with
respect to an indistinguishability operator E on X. Then:
m
r
(
µ
,
ν
) H
E
.
Corollary 1 Let
µ
be a fuzzy subset on a set X and E an indistinguishability oper-
ator. Then:
m
r
(
φ
E
(
µ
),
ψ
E
(
µ
)) H
E
.

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### Cites background or methods from "Comparison of Different Algorithms ..."

• ...Fuzzy sets φE(μ) and ψE(μ) were introduced to provide upper and lower approximation of a fuzzy set μ with respect to fuzzy equivalence relation E ....

[...]

• ...Extensional fuzzy subsets have been widely studied in the literature , , , ....

[...]

##### References
More filters

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TL;DR: The book is a solid reference for professionals as well as a useful text for students in the fields of operations research, management science, industrial engineering, applied mathematics, and also in engineering disciplines that deal with analytical optimization techniques.
Abstract: COMPREHENSIVE COVERAGE OF NONLINEAR PROGRAMMING THEORY AND ALGORITHMS, THOROUGHLY REVISED AND EXPANDED"Nonlinear Programming: Theory and Algorithms"--now in an extensively updated Third Edition--addresses the problem of optimizing an objective function in the presence of equality and inequality constraints. Many realistic problems cannot be adequately represented as a linear program owing to the nature of the nonlinearity of the objective function and/or the nonlinearity of any constraints. The "Third Edition" begins with a general introduction to nonlinear programming with illustrative examples and guidelines for model construction.Concentration on the three major parts of nonlinear programming is provided: Convex analysis with discussion of topological properties of convex sets, separation and support of convex sets, polyhedral sets, extreme points and extreme directions of polyhedral sets, and linear programmingOptimality conditions and duality with coverage of the nature, interpretation, and value of the classical Fritz John (FJ) and the Karush-Kuhn-Tucker (KKT) optimality conditions; the interrelationships between various proposed constraint qualifications; and Lagrangian duality and saddle point optimality conditionsAlgorithms and their convergence, with a presentation of algorithms for solving both unconstrained and constrained nonlinear programming problemsImportant features of the "Third Edition" include: New topics such as second interior point methods, nonconvex optimization, nondifferentiable optimization, and moreUpdated discussion and new applications in each chapterDetailed numerical examples and graphical illustrationsEssential coverage of modeling and formulating nonlinear programsSimple numerical problemsAdvanced theoretical exercisesThe book is a solid reference for professionals as well as a useful text for students in the fields of operations research, management science, industrial engineering, applied mathematics, and also in engineering disciplines that deal with analytical optimization techniques. The logical and self-contained format uniquely covers nonlinear programming techniques with a great depth of information and an abundance of valuable examples and illustrations that showcase the most current advances in nonlinear problems.

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Abstract: There are three basic concepts that underlie human cognition: granulation, organization and causation. Informally, granulation involves decomposition of whole into parts; organization involves integration of parts into whole; and causation involves association of causes with effects. Granulation of an object A leads to a collection of granules of A, with a granule being a clump of points (objects) drawn together by indistinguishability, similarity, proximity or functionality. For example, the granules of a human head are the forehead, nose, cheeks, ears, eyes, etc. In general, granulation is hierarchical in nature. A familiar example is the granulation of time into years, months, days, hours, minutes, etc. Modes of information granulation (IG) in which the granules are crisp (c-granular) play important roles in a wide variety of methods, approaches and techniques. Crisp IG, however, does not reflect the fact that in almost all of human reasoning and concept formation the granules are fuzzy (f-granular). The granules of a human head, for example, are fuzzy in the sense that the boundaries between cheeks, nose, forehead, ears, etc. are not sharply defined. Furthermore, the attributes of fuzzy granules, e.g., length of nose, are fuzzy, as are their values: long, short, very long, etc. The fuzziness of granules, their attributes and their values is characteristic of ways in which humans granulate and manipulate information.

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