< prev

Page 1Page 2Page 3Page 4Page 5Page 6Page 7Page 8

Page 2 of 8
next >

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