Majalah Ilmiah UNIKOM
Vol.9, No. 1
44
H a l a m a n
types of uncertainty that emerge
from the imprecise knowledge natural
state are :
Measurement uncertainty. It is the er-
ror on observed quantities
Process uncertainty. It is the dynamic
randomness.
Model uncertainty. It is the wrong speci-
fication of the model structure
Estimate uncertainty. It is the one that
can appear from any of the previous
uncertainties or a combination of them,
and it is called inexactness and impre-
cision.
Implementation uncertainty. It is the
consequence of the variability that re-
sults from sorting politics, i.e. incapac-
ity to reach the exact strategic objec-
tive.
This paper presents development and
design of a GUI toolbox for construction,
edition and observation of interval type-2
Fuzzy Inference Systems. The toolbox cov-
ers all phases of Interval Type 2 Fuzzy
Logic Toolbox from first phase till the last.
The toolbox best qualities are the flexibility
which enables the user to add new file and
user friendly which makes it suitable for
versatile range of user from beginner to
advance.
In Section 2 some topics on Interval
Type 2 Fuzzy Logic System is presented, in
Section 3 developed loolbox is demon-
strated; in Section 4 the toolbox is ex-
ploited for water level problem. Finally,
conclusion are stated in Section 5.
T2TSK STRUCTURE
Type-2 fuzzy sets have a fuzzy member-
ship function, modeling the imprecise na-
ture of a fuzzy membership grade. As the
field has developed, two main categories
of type-2 fuzzy set have emerged; general-
ized and interval. Generalized type-2 fuzzy
sets model a fuzzy membership grade as a
fuzzy number between zero and one. Type-
2 interval fuzzy sets model a fuzzy mem-
bership grade as a crisp interval in [0,1].
Type-2 interval fuzzy sets are a limited
version of the generalized type-2 fuzzy set
where the secondary membership grade is
always 1. This limitation allows type-2 in-
terval sets to be processed a great deal
more quickly than generalized type-2 fuzzy
sets.
An interval type-2 fuzzy logic system
contains five components—fuzzifier, rules,
inference engine, type-reducer and de-
fuzzifier—that are inter-connected, as
shown in Fig. 1. Once the rules have been
established, a FLS can be viewed as a
mapping from input to output.
The Interval type-2 fuzzy logic system
works as follows: the crisp inputs are first
fuzzified into either type-0 (known as sin-
Muhammad Aria
Fig. 1. Type 2 Fuzzy System Structure