Advances in Pure Mathematics
Vol.07 No.07(2017), Article ID:77480,13 pages
10.4236/apm.2017.77022
Fuzzy Logic and Zadeh Algebra
Paavo Kukkurainen
School of Engineering Science, Lappeenranta University of Technology, Lappeenranta, Finland
Copyright © 2017 by author and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).
http://creativecommons.org/licenses/by/4.0/
Received: May 22, 2017; Accepted: July 4, 2017; Published: July 7, 2017
ABSTRACT
In this work we create a connection between AFS (Axiomatic Fuzzy Sets) fuzzy logic systems and Zadeh algebra. Beginning with simple concepts we construct fuzzy logic concepts. Simple concepts can be interpreted semantically. The membership functions of fuzzy concepts form chains which satisfy Zadeh algebra axioms. These chains are based on important relationship condition (1) represented in the introduction where the binary relation of a simple concept m is defined more general in Definition 2.10. Then every chain of membership functions forms a Zadeh algebra. It demands a lot of preliminaries before we obtain this desired result.
Keywords:
AFS Fuzzy Logic System, Zadeh Algebra, Simple Concepts, Membership Functions
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.
Cite this paper
Kukkurainen, P. (2017) Fuzzy Logic and Zadeh Algebra. Ad- vances in Pure Mathematics, 7, 353-365. https://doi.org/10.4236/apm.2017.77022
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