Biomedical Engineering Reference
In-Depth Information
character recognition. In the following section, we describe the basic prop-
erties of fuzzy sets and their extension to fuzzy logic applications. Readers
interested in further details can consult many other resources on fuzzy theory
and applications (Zadeh, 1965, 1973; Zadeh et al., 2002; Brubaker, 1992).
3.5.1 Fuzzy Sets
A fuzzy set is defined as a group or collection of elements with membership
values between 0 (completely not in the set) and 1 (completely in the set).
The membership values give the property of fuzziness to elements of a fuzzy
set in that an element can partially belong to a set. The following definition
formalizes the fuzzy set.
DEFINITION 3.5.1 (Fuzzy Set [Zadeh, 1965]) A fuzzy set is character-
ized by a membership function, which maps the elements of a domain, space,
or an universe of discourse X to the unit interval [0 , 1] , written as
A = X
[0 , 1]
Hence a fuzzy set A is represented by a set of pairs consisting of the element
x
X and the value of its membership and written as
A =
{
m ( x )
|
x
X
}
A crisp set is our standard set where membership values take either 0 or 1.
Fuzzy sets can be defined on finite or infinite universes where when the uni-
verse is finite and discrete with cardinality n , the fuzzy set is an n -dimensional
vector with values corresponding to the membership grades and the position
in the vector corresponding to the set elements. When the universe is contin-
uous, the fuzzy set may be represented by the integral
A =
x
m ( x ) /x
(3.60)
where the integral shows the union of elements not the standard summation
for integrals. The quantity m ( x ) /x denotes the element x with membership
grade m ( x ).
An example of a fuzzy set is the set of points on a real line (Pedrycz and
Vasilakos, 2001). Their membership functions can be described by a delta
function where for x, p
, the membership function is
p )= 1 f x = p
0 f x = p
m ( x )= δ ( x
(3.61)
http://austinlinks.com/Fuzzy/
Search WWH ::




Custom Search