1. Introduction
Starting with simple concepts such as “young people” or “tall people” it is possible to form AFS logic system
. The elements are fuzzy concepts constructed by simple concepts. Notice that
is a comple- tely distributive lattice and is called the
(expanding one set M) algebra over
. So the AFS logic system is a completely distributive lattice equipped with the logical negation
. Let
be a non-empty set. For any
, let
be a membership function of the concept
. Moreover, we assume that all the elements
in the set
satisfy the three conditions, Definition 2.17. Consider a binary relation
of the concept
. For example, for any two persons
and
,
if and only if
where
is a fuzzy concept “old”. The exact definition is represented in Definition 2.10. In Section 2 all the results are known and can be found from [1] . Also the used examples are there. For Zadeh algebra axioms we refer to [2] . In Section 3 it is proved new results. But all the preliminaries represented in Section 2 are necessary to know for understanding these results and their proofs. The crucial condition is
(1)
for simple concepts
and for all the pairs
and
in
. In fact, the condition determines a chain
, Lemma 3.1 (c). Let
be a set of membership functions of the concept
of the AFS fuzzy logic system
. According to Proposition 3.4 a chain
corres- ponding to the chain
satisfies the seven Zadeh algebra axioms and then forms some Zadeh algebra, Proposition 3.5. These are the two main results. Observing that by Lemma 3.1 (a) the condition (1) implies the condition (2) needed in Proposition 3.4.
(2)
In the conclusion it is illustrated the research motivation and contribution of this paper.
2. Preliminaries
2.1. Lattices
In this subsection we refer to [3] , pages 1, 2, 6, 8, 9, 10, 119 and [1] , pages 61-64, 67, 77.
Definition 2.1. A partially ordered set or a poset is a set in which a binary relation
is defined satisfying the following conditions (P1)-(P3):
(P1) For all
,
.
(P2) If
and
, then
.
(P3)
and
, then
.
Let
(P4) Given
and
, either
or
.
A poset which satisfies (P4) is said to be linearly ordered and is called a chain.
Let
be a subset of a poset
. Denote the least upper bound of
by l.u.b. i.e.
and the greatest lower bound of
by g.l.b. i.e.
.
Definition 2.2. A lattice
is a poset
where any two of whose elements
and
have g.l.b. or a meet denoted by
, and l.u.b. or a join denoted by
. A lattice
is complete if each of its subsets has l.u.b. and g.l.b. in
.
It is clear that any nonvoid complete lattice contains a least element 0 and a greatest element 1.
In any lattice
(or a poset), the operations
and
satisfy the following laws, whenever the expressions are refered to exist:
(L1)
,
(L2)
,
(L3)
,
(L4)
Conversely, any system
with the two binary operations satisfying (L1) - (L4) is a lattice.
Moreover,
is equivalent to each of the conditions
If a poset
(or a lattice) has an 0, then
and
for all
. If
has a universal upper bound
, then
and
for all
.
Definition 2.3. A lattice
is distributive if and only if the conditions
hold in
. In fact, these conditions are equivalent if they are valid.
Definition 2.4. [1] , pages 77, 116 or [3] , page 119
Let
be a complete lattice. Then
is called a completely distributive lattice if it satisfies the extended distributive laws: for any family
where
and
are non-empty indexing sets, the following equations are valid
2.2. A Survey to Simple Concepts and Their Operations
In this subsection we approach to simple concepts and their operations because it is necessary to form the idea what do simple concepts mean. The exact definition will be represented in Definition 2.13. All these are based on [1] , pages 113,114.
Consider the set of four people
and a simple concept “hair colour”. By intuition, we may set:
has “hair black” with number 6 and
with numbers 4,6,3. So, the numbers imply the order
which can be interpreted as follows: Moving from right to left, the relationship states how strongly the hair colour resembles black colour. More exactly,
means that the hair of
is closer to the black colour than the colour of the hair which
has.
Let
be a set of fuzzy or Boolean concepts on the set
. For each
we associate to a single feature. For example
: “old people” is a fuzzy concept but
: “male” is a Boolean concept. In fact,
is a set of simple concepts. In general let
and denote by
a conjugation of the concepts
on
. Correspondingly
means a disjunction.
Example 2.5. Let
: “old people”,
: “male”,
: “tall people”. Then
: “old males” and
: “old or tall people”. Further,
: “old or tall males”. However,
means the same. This is because for any person
the degree of
belonging to the fuzzy concept represented by
is always less than or equal to the degree of
belonging to the fuzzy concept represented by
or
. Therefore the former
is including in both of the latter ones
or
.
2.3. AFS Fuzzy Logic System
All the definitions and the propositions with their proofs are represented in [1] , pages 115-123. For a moment we give up the assumption that
consists only of simple concepts. Let
be a non-empty set. The set
is defined by
where the elements of
are expressed semantically with “equivalent to”, “or” (disjunction) and “and” (conjunction).
Definition 2.6. Let M be a non-empty set. A binary relation R on
is defined as follows: for
,
,
(1)
,
,
,
such that
,
(2)
,
,
,
such that
.
is an equivalence relation and we define
as the quotient set
.
Proposition 2.7. Let
be a non-empty set. Then
forms a completely distributive lattice under the binary compositions
and
defined as follows: for any
where the disjoint union
means that every element in
and every element in
are always regarded as different elements in
. Therefore for any
,
if
, and
if
.
The proof of the proposition can be found from [1] .
To be a distributive lattice means that for any
A completely distributive lattice is defined in Definition 2.4. Because
is such a lattice it guarantees the existance of the
elements
and
. We can also define the order in
as follows:
Further, as a (distributive) completely lattice
is also a complete lattice.
The lattice
is called the
(expanding one set
) algebra over
.
Proposition 2.8. Let
be a set and
be a map satisfying
for all
. If the operator
is defined as follows
Then for any
,
has the following properties:
(1)
,
(2)
,
,
(3)
Therefore the operator
is an order reversing involution in the
algebra
.
The operator
defines the negation
of the concept
:
. Then
.
Let
. Then
stands for the logical negation of
.
is called an AFS fuzzy logic system.
Example 2.9. Let
: “old people”,
: “tall people”,
: “males”. Then
where
: “old males or tall people” and
: “not old and not tall people or not tall males”.
The AFS fuzzy logic system
can be regarded as a completely distributive lattice. It is also a complete lattice. But this lattice is equipped with the logical negation.
We conclude that the complexity of human concepts is a direct result of the combinations of a few relatively simple concepts. In fact, some suitable simple concepts play the same role as used in linear vector spaces and we can regard them as a “basis”.
2.4. Relations, Simple and Complex Concepts
For this subsection we refer to [1] , pages 124, 125.
Definition 2.10. Let
be any concept on the universe of discourse
.
is called the binary relation of the concept
if
satisfies:
if and only if x belongs to concept
at some extent or
is a member of
and the degree of
belonging to
is larger or equal to that of
, or
belongs to concept
at some degree and
does not at all.
Example 2.11. Let fuzzy concept
: “old” and
Therefore
means that x belongs to
at some degree and that
means that
does not belong to
at all. If for the two persons
and
,
and
then
but
.
Example 2.12. Let fuzzy concept
: “hair black” and define
in the corresponding way as above. By human intuition, we assume that for the three persons
the degree of
is the following:
but the fourth person
has no hairs. Then
and
but
. See Definition 2.13 (2).
Definition 2.13. Let
be a set and
be a binary relation on
.
is called a sub-preference relation on
if for
,
satisfies the following conditions:
(1) if
, then
,
(2) if
and
, then
,
(3) if
and
, then
,
(4) if
and
, then either
or
.
We define that a concept
on
is simple if
is a sub-preference relation on
. Otherwise
is called a complex concept on
.
Example 2.14. Let
: “old people”. The concept is simple. For example if for the persons
we have
is a sub-preference relation on the set
where
is the binary relation defined in Example 2.11. Observe that the latter of the assumptions of (2) in Definition 2.13 is not valid and so the condition (2) is valid. In general it is known that all elements belonging to a simple concept at some degree are comparable and are arranged in a linear order, that is, they form a chain. In above we can think shortly that
.
Further, there exists a pair of different elements belonging to a complex concept at some degree such that their degrees in this complex concept are incomparable.
Example 2.15. The set
consists of disjoint sets
: “males” and
: “females”. The concept
: “beautiful” is simple on
and on
:
However, if we apply
to the whole set
it is a complex concept because the degrees of the elements
and
may be incomparable:
If
and
, then
and If
and
, then
In this case
and
implies that both
and
. The condition (4) in Definition 2.13 is not satisfied and so
is a complex concept.
2.5. The AFS Fuzzy Logic and Coherence Membership Functions
For introduction to characteristic and membership functions we refer to [4] , page 255, and [5] , pages 12-18. Definitions 2.16 and 2.17 can be found from [1] , pages 128, 130. We first become acquainted with concepts fuzzy sets and membership functions.
Let
and
,
. Define a characteristic function for the set
as follows:
Consider an extended case
, that is,
is also possible. We call for the set
(a) a crisp set, if its characteristic function is
,
(b) a fuzzy set, if its extended characteristic function or a membership function is
.
Therefore for every element
there is a membership degree
. The set of pairs
determines completely the fuzzy set
. The characteristic function of a crisp set
is a special case of a membership function
.
Definition 2.16. [1] Let
,
be sets and
be the power set of
. Let
.
is called an AFS structure if
satisfies the following axioms:
(1)
,
(2)
.
We again return to the case that
is a set of simple concepts.
Let
be a set of objects and
be a set of simple concepts on
.
is defined as follows: for any
where
is the binary relation of simple concepts
defined in Definition 2.10 (it was defined more general than for simple concepts).
It is proved in [1] that
is an AFS structure.
Definition 2.17. [1] Let
be an AFS structure of a data set
. For
, the set
is defined as follows:
For
, let
be the membership function of the concept
.
is called a set of coherence membership functions of the AFS fuzzy logic system
and the AFS structure
, if the following conditions are satisfied:
(1) For
, if
in lattice
, then
for any
.
(2) For
, if
for all
then
.
(3) For
, if
, then
; if
then
.
Remark: It is important to see that
consists of simple elements
.
2.6. Zadeh Algebra
We refer to [2] .
Definition 2.18. Suppose that
is a complete distributive lattice. Let
be the set of all functions from
to
. Assume that the lattice operations the least upper bound
and the greatest lower bound
on
are extended pointwise for the functions on
. Further, define the extreme constant functions
,
and
for all
, where
and
are the least and the greatest elements of
, respectively. A unary operation
on
satisfies the involution property for any
, and
is extended pointwise for the functions on
, i.e.,
for any
. Then
is called Zadeh algebra if it satiesfies the following conditions:
(Z1) The operations
and
are commutative on
.
(Z2) The operations
and
are associative on
.
(Z3) The operations
and
are distributive on
.
(Z4) The neutral elements of the operations
and
are
and
, respectively, i.e., for all
and for all
,
and
.
(Z5) For any function
and for all
, there exists
such that
, i.e.,
is order revers- ing.
(Z6)
.
(Z7) Zadeh algebra fulfils the Kleene condition: for any function
and for any
,
, where
and
are the logical negations of
and
, respectively.
3. Connection between Coherence Membership Functions of the AFS Fuzzy Logic System
and Zadeh Algebra
Lemma 3.1. Let
and
be elements in
where concepts
are simple and let
be a non-empty set. If every relation
satisfies the condition
(1)
for pairs
and
, then
(a)
(b) there exists
in the set
such that
for every
, that is,
(c) Let
,
,
be the elements in
such that the condition (a) is satisfied for all pairs
. Then
.
Proof. Assume that the condition (1) holds.
(a) Let
. Then
. Because
is simple,
is a sub- preference relation and by Definition 2.13 (3) it is transitive. This implies that
and (a) is valid.
(b) Let
Because
is simple
and
are defined.
Because
we conclude that
and so there exists
in the set
such that for every
is
. If there exists
which does not contain
then
does not hold. This is a contradiction. According to discussion after Proposition 2.7 we have
(c) Consider a pair
, where
and
. We will prove that
, that is,
if
(condition (1)).
Repeating the proof of (b) we obtain the following: Let
,
Then
and there exists
in the set
such that
for all
. This proves
. In the same way we can prove that
for a pair
and then we conclude that
□
Lemma 3.2. Let
. If the condition
holds for every simple concept
then
where
is the membership function of the concept
.
Proof. Let
be simple concepts and
Assume that the condition
holds for every
. By Lemma 3.1 (a)
also holds. Then
and so
It follows that
.
Let
. These
exist because
is a completely lattice. By Definition 2.17 (3)
for every
where
is a membership function. Let
. Also
exists and we obtain
□
Lemma 3.3. Assume that the binary relations
of simple concepts
satisfy the condition
for pairs
.
Let
and
be elements in
. Then
and membership functions
and
satisfy the Kleene condition
Proof. By Lemma 3.1 (b),
. On the other hand, by Lemma 3.2
Because
, by Definition 2.17 (1),
. We obtain
□
Proposition 3.4. Let
be a set of membership functions of the AFS fuzzy logic system
. The
elements
are of the form
Let
be binary relations of simple concepts
. If the condition
is valid for every simple concept
and
then the mem- bership functions
satisfy the conditions (Z1) - (Z7) of Zadeh algebra in Definition 2.18.
Proof. We verify the Zadeh algebra axioms: The first three axioms (Z1) - (Z3) are clear.
(Z1) The operations
and
are commutative on
.
(Z2) The operations
and
are associative on
.
(Z3) The operations
and
are distributive on
.
(Z4) The neutral elements of the operations
and
are
,
and
,
.
(Z5) For any
and for all
there exists a unary opera- tion
where the operation
is defined by
Let
be simple concepts. Observe that we need this assumption for Definition 2.17 used bellow: in Definition 2.16 and Definition 2.17 the definition of
demands
to be simple. According to Proposition 2.8
is an order reversing involution and
but in this case
need not be simple. We obtain
Therefore
is an involution. Here
is not necessary simple.
Let
be elements in
, and since
is order reversing,
. Using Definition 2.17 (1) it is
. Therefore
and
is order reversing.
(Z6)
for all
(Z7) The Kleene condition. For any function
and for any
we have
which is proved in Lemma 3.3. □
Proposition 3.5. Let
be a set of membership functions
of the AFS fuzzy logic system
. The
elements
are of the form
Let
be binary relations of simple concepts
. If the condition
is valid for every simple concept
and
then
(a) Functions
form a chain corresponding to the chain
.
(b) Any chain
constitutes some Zadeh algebra
.
Proof. We conclude
(a) By Lemma 3.1 the elements
forms a chain
. By Definition 2.17 (1)
.
(b) Lemma 3.1 (a) implies that
and in Proposition 3.4 it is proved that every chain
satisfies (Z1) - (Z7). □
4. Conclutions
Simple concepts form chains. The elements of any chain form a “basis” in AFS fuzzy logic system
with operations disjunction
, conjunction
and the logical negation. The elements are of the form
where simple concepts
are defined in Definition 2.13 and operations in
are defined in Proposition 2.7.
is a completely distributive lattice. By means of the binary relations
defined in Definition 2.10 we construct the condition
which implies the condition
. Here
is a non-empty set and
on
and
are simple concepts. Then the conditions consti- tute the two things: first, the membership functions
of the fuzzy concepts
form chains
in
; second, every chain
forms a Zadeh algebra. These results are represented in Propositions 3.4 and 3.5 and we can use them as starting points to continue theoretical considerations. The other way to continue the investigations is to utilize directly the conditions above: there are two kinds of successive events. The first one implies the second one or they have no connection. In the latter case the second event only follows the first one although they are independent of each other. In the first case it is possible to apply to the conditions (above) (1) or (2) repre- sented in the introduction.
In Example 2.1, [2] , the set
of membership functions forms a Zadeh algebra. More exactly, if
then
is a complete distributive lattice. Further,
is the set of membership functions. The operations
and
are extended pointwise on
. Now
is a Zadeh algebra with
,
, and the logical negation of
is
. In this paper we have considered more general membership functions and constructed Zadeh algebras.