Majalah Ilmiah UNIKOM
Vol.10 No. 1
67
H a l a m a n
Fuzzy as discussed in [5] – [7], as far as the
author concerns, there is no work on
development of type-2 Fuzzy is suitable for
elevator group control. Contribution of this
paper is to propose type-2 fuzzy algorithm
suitable for elevator group control and
compare the performance between the
interval type 2 Fuzzy and type 1 Fuzzy.
The paper is arranged as follows. Type-2
Fuzzy structure is provided in Section II. In
Section III we describe the elevator group
control system and the area-weight of the
control system. Fuzzy model to determine
the area-weight is presented in Section IV.
Its performance is analyzed through the
simulation in Section V. In Section VI, we
conclude with conclusion.
II. TYPE-2 FUZZY STRUCTURE
Type-2 fuzzy sets were originally
presented by Zadeh in 1975. The new
concepts were introduced by Mendel and
Liang allowing the characterization of a type-
2 fuzzy set with a superior membership
function and an inferior membership
function; these two functions can be
represented each one by a type-1 fuzzy set
membership function. The interval between
these two functions represent the footprint
of uncertainty (FOU), which is used to
characterize a type-2 fuzzy set. Type-2 fuzzy
sets allow us to handle linguistic
uncertainties, as typified by the adage
“words can mean different things to
different people”.
For type-2 TSK models, there are three
possible structure [11]:
1. Antecedents are type-2 fuzzy sets, and
consequents are type-1 fuzzy sets. This
is the most general case and we call it
Model I.
2. Antecedents are type-2 fuzzy sets, and
consequents are crisp number. This is
special case or Model I and we call it
model II.
3. Antecedents are type-1 Fuzzy sets and
consequents are type-1 fuzzy sets. This
is another special case of Model I and we
call it Model III.
We use Model I to design interval type-2
TSK Fuzzy system in this paper. A schematic
diagram of the proposed T2TSK structure is
i
m
Rule Base
In a first-order type-2 TSK Model I with a
mn
denoted as
where
and
. The
consequent parameter
, which are type-1 fuzzy sets, has interval, is
denoted as
T h e
m e m b e r s h i p
g r a d e s
are interval sets to, which denoted as
Where
is lower membership function
and
is upper membership function.
These rules let us simultaneously account
for
uncertainty
about
antecedent
membership functions and consequent
parameter values.
Fuzzification
This process is transforming the crisp
input to a type-II fuzzy variable. The primary
membership functions for each antecedent
Muhammad Aria.