Fuzzy Logic In Statistics

2531 Words11 Pages
Ali Zeshan 123001
Hamza Ashfaq 123004
Arsalan Taj 123025
Ehtisham Bukhari 123064

In this report a novel control that optimizes passenger service in an elevator group is described.
Fuzzy logic and artificial intelligence are applied in the control when allocating landing calls to the elevators. Fuzzy logic is used to recognize the traffic pattern and the traffic peaks from statistical forecasts. In order to form the statistical forecasts, the passenger traffic flow in the building is measured. In the statistics the passenger traffic flow is learned day by day, and the control adapts to the prevailing traffic situation. The validity of the forecast data is confirmed
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In the inter-floor traffic the passengers travel from one populated floor to another inside the building.

Real traffic patterns during a day are combinations of these three traffic components. A statistical forecast for a typical day in a single tenant office building with common working hours is shown in Figure 4. The three passenger traffic components were forecast by the TMS9000 control.
According to the figure, traffic intensity is highest in the morning at 8:30 a.m. and during the lunch hour at 12:00 a.m. During the morning up-peak, people arrive at work and it is the most demanding time for the elevator handling capacity. A lot of inter-floor traffic in the morning has also been measured. During the lunch hour there is typically about 40 per cent incoming, 40 per cent outgoing, and 20 per cent inter-floor traffic. The lunch hour traffic is the most demanding for the group control capability since there are a lot of car and landing calls to be served. In the evening, people exit the building, and mostly outgoing traffic is forecast.

TRAFFIC PATTERN RECOGNITION WITH FUZZY LOGIC

Inputs and
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The traffic component values show the portions of the three components as percentages of the total traffic volume at the defined time. The incoming traffic component value, u1, is

u1 = 100 *linc / (linc + lout + l int er - floor)

where linc, lout and linter-floor are the passenger arrival rates for incoming, outgoing and interfloor traffic, respectively. Analogous equations for the outgoing (u2) and inter-floor (u3) traffic component values exist.

In addition to the traffic type, the number of arriving passengers in a defined time affects the traffic pattern. Obviously, the same absolute passenger arrival rates cannot be used to recognize a traffic peak for every building. The relative intensity that takes into account the building and elevator group configuration is obtained by scaling the arrival rate to the up-peak handling capacity of the elevator group. The relative traffic intensity value is

u4 = 100*(linc + lout + l int er - floor) / HC

where HC is the up-peak handling capacity of the elevator group (Barney et al. 1985; Roschier et al. 1979). The input values of passenger arrival rates (linc, lout and linter-floor) and the
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