Majalah Ilmiah UNIKOM
Vol.10 No. 1
73
H a l a m a n
Figure 11. Membership Function of Long Wait
Probability
k
Table 2. Area Fuzzy Rules
Fuzzy Model
We propose a fuzzy model to determine
the area-weight through a two step fuzzy
inference. Two step inference mechanism
improves the system’s stability from external
accidents and reduces the complexity of the
system. Figure 13 shows the two step fuzzy
inference mechanism.
Figure 13. The Fuzzy model to determine the area-
weight
In step 1 of the fuzzy inference engine, the
predetermined
area-weight
(
)
is
calculated using the up-going (UP) and down
-going (DN) traffic. In step 2, the adjustment
value
is determined through the fuzzy
inference mechanism using the average
waiting time (AWT), the long wait probability
(LWP) and the power consumption values
(PC). This value is added to the
predetermined area-weight.
V. SIMULATION RESULTS AND DISCUSSION
Performance of Type 2 Fuzzy Controller is
investigated using simulation studies. The
developed controller was compared with
Type-1 Fuzzy Controller. The condition of
simulation are shown in the Table 3.
According to the traffic pattern, the
simulation situation is divided into several
periods such as before lunch time (12:00 –
12:40), after lunch time (12:40 – 13:20)
and common time (13:20 – 15:00). The
evaluation criteria is the means of the
average waiting time (AWT), power
consumptions (PC) and average long wait
probabilities (LWP). In the simulation, the
Muhammad Aria
Inference Rules
Weight
k
1
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1
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1
k
1
k
1
k
1
k
1
k
1
k
1
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1
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1
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1
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1