Nasir Ahmed Mengal BUETK

4. Introduction...
Fuzzy set:
Fuzzy sets are sets whose elements have degrees of
membership. Fuzzy sets were introduced simultaneously
by Lotfi A. Zadeh and Dieter Klaua in 1965 as an extension
of the classical notion of set. In classical set theory, the
membership of elements in a set is assessed in binary terms
according to a bivalent condition — an element either
belongs or does not belong to the set.
By contrast, fuzzy set theory permits the gradual
assessment of the membership of elements in a set; this is
described with the aid of a membership function valued in
the real unit interval [0, 1].

5. Fuzzy sets generalize classical sets, since the indicator
functions of classical sets are special cases of the
membership functions of fuzzy sets, if the latter only take
values 0 or 1. In fuzzy set theory, classical bivalent sets are
usually called crisp sets. The fuzzy set theory can be used in
a wide range of domains in which information is
incomplete or imprecise, such as bioinformatics.
Examples of fuzzy sets include: {‘Tall people’}, {‘Nice day’},
{‘Round object’} …
If a person’s height is 1.88 meters is he considered ‘tall’?
What if we also know that he is an NBA player?

6. Evidence
Pattern
Theory
Recognition
& Image
Processing
Fuzzy
Logic &
Fuzzy Set
Theory
Knowledge
Engineering
Control
Theory

7. Input_1 Fuzzy
IF-THEN Output
Input_2
Rules
Input_3

8. Fuzzy vs Probability
Walking in the desert, close to being dehydrated, you
find two bottles of water:
The first contains deadly poison with a probability of 0.1
The second has a 0.9 membership value in the Fuzzy Set
“Safe drinks”
Which one will you choose to drink from???

9. Summary
• Fuzzy Logic can be useful in solving Human related tasks.
• Evidence Theory gives tools to handle knowledge.
• Membership functions and Aggregation methods can be
selected according to the problem at hand.