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UNIVERSITI KUALA LUMPUR

BRITISH MALAYSIAN INSTITUTE

BACHELOR OF ENGINEERING TECHNOLOGY

ARTIFICIAL INTELLIGENCE

(BEB 45104)

SEMESTER MAY 2012

ASSIGNMENT 1

FUZZY LOGIC

STUDENT NAME

MASTERJI AI ID NO

GIRLY AI ID NO

GROUP:

L01

LECTURER NAME:

NARUTO SHIPPUDEN

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1.0 INTRODUCTION

Fridge is a common household appliance that consists of a thermally

insulated compartment and a heat pump (mechanical, electronic, or chemical) that transfers heat

from the inside of the fridge to its external environment so that the inside of the fridge is cooled

to a temperature below the ambient temperature of the room. Cooling is a popular food storage

technique in developed countries and works by decreasing or even arresting the reproduction rate

of bacteria. The device is thus used to reduce the rate of spoilage of foodstuffs.

A refrigerator maintains a temperature a few degrees above the freezing point of water.

Optimum temperature range for perishable food storage is -10 to 25 °C (30.2 to 77 °F).A similar

device which maintains a temperature below the freezing point of water is called a fridge.

2.0 PROBLEM STATEMENT

As well we know fridge is a one of electric device which is high power consumption. The

fuzzy fridge temperature control is designed to control the temperature of fridge to give the

fridge low power consumption and more efficiency. The fuzzy logic use 3 power settings to

match the temperature in the refrigerator with the amount of food stored. Full power operation is

selected when the door opened and closed frequently, while operating at minimum power

selected when it is rarely open and closed to reduce electricity consumption dramatically.

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3.0 PROJECT DISCRIPTION

Fuzzy Fridge Control System

In a this brief, this system will be control temperature that needed that suitable for fridge,

due to the rise in temperature resulted from receiving a constant high humidity produced by

fridge environment , after few hours.

This system monitoring temperature control mechanism as it has capability to constantly

self adjusting power corresponding with the variation of temperature, humidity within the in

fridge. Therefore, fridge’s temperature and humidity will be maintained at adequate and

permissible level to ensure that the fridge will have a coolest provided at all times.

Figure 3 : Some of fridge type

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4.0 THE LINGUISTIC VARIABLES

Linguistic variable represents, in words, the input variables and output variables of the

system to control. In this system, three input linguistic variables and one output variable have

been defined as listed in Table 4

Input Linguistic Variables

1 Temperature

2 Humidity

3 Weight

Output Linguistic Variables

1 Power

Table 4: Linguistic Variables

5.0 FUZZY SETS AND MEMBERSHIP FUNCTIONS

Input Linguistic Variables Min Max Range Unit

1 Temperature -10 25 -10 to 25 ◦C

2 Humidity 30 80 30 to 80 %

3 Weight 0 30 0 to 30 kg

Output Linguistic Variables

1 Power 140 250 140 to 250 kW

Table 5: Linguistic Values

Input Linguistic Variables Fuzzy Sets

1 Temperature {low,medium,high}

2 Humidity {low,medium,high}

3 Weight {light,normal,heavy}

Output Linguistic Variables

1 Power {low,medium,high}

Table 5.1: Fuzzy Sets

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The designated membership functions for each variable is as follows:

Figure 5: Temperature (◦C)

Figure 5.1: Humidity (%)

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Figure 5.2 : Weight

Figure 5.3 : Power (kW)

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6.0 FUZZY RULES AND RESULTING FUNCTION GRAPH

The total of 9 rules has been constructed for this system, as in Table 6.

Table 6 : Fuzzy Rules

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6.1 3D FUNCTION GRAPH

The 3D graph result Temperature Vs Weight on Labview application designed.

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The 3D graph result Temperature Vs Humidity Labview application designed.

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7.0 Tuning The System

We are using LabView to design a fuzzy system as a fuzzy engine that are shown in Figure 7

below.

Figure 7 : Labview

Yellow (Low Condition)

Blue (Medium Condition)

Red (High Condition)

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SAMPLES RESULTS OBTAINED FROM FIRST TESTING

1st Test Condition

1st Test Condition

Input Variable

Temperature 12 °C

Humidity 35 %

Weight 3 kg

Output Variable

Light Indicator Yellow

Results

Successfully

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2nd Test Condition

2nd

Test Condition

Input Variable

Temperature 5 °C

Humidity 47 %

Weight 14 kg

Output Variable

Light Indicator Blue

Results

Successfully

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3rd Test Condition

3rd

Test Condition

Input Variable

Temperature 15 °C

Humidity 70 %

Weight 26 kg

Output Variable

Light Indicator Red

Results

Successfully

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8.0 ANALYSIS

Basically, in this system humidity, temperature, and weight are related as follows:

At lowest humidity, at lowest temperature, and light weight the power will be at its

lower. In this condition if when:

- Humidity drops, the temperature will be reduced and weight is light

- Power will set to the lowest .

At medium temperature, at medium humidity, and medium weight the power will set

to medium. In this condition if when:

- Humidity increases from lowest to medium, the temperature will be increased and

weight is medium.

At highest humidity, at highest temperature, and heavy weight the power will

switching to maximum power in assure that fridge will be in cooler condition . In this

condition if when:

- Humidity highest, the temperature will be increased and the weight is maximum

condition.

Thus it can be summarized that, the fridge cooler is directly proportional to the

humidity , temperature and also weight

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9.0 CONCLUSION

From this project designed, we have learned a lot of things. Especially, about how to use

the Labview 2011 to completing the project as requested. By the use of fuzzy logic control, we

have been able to control the temperature of fridge in automatically with different of speed and

temperature in the fridge. In directly the fridge is become more efficiency and low power

consumption.

Fuzzy logic and, which have been applied in various interesting areas, such as the control

of complex and imprecisely defined processes, have been used in this study to develop a fuzzy

controller to test and evaluate the performance of fridge at the cooler stage. The fuzzy models

turned out to be a simple, and gave time saving as compared to the classical approaches. They

also provided a good representation of the human expertise in terms of the use of linguistic terms

and fuzzy rules and also making available a some of decisions. Future research should deal with

the tuning and improvement of the fuzzy model to guarantee a real competition and possibly

superiority over the human experts in terms of the achievement of the smallest possible

temperature and the time needed for the achievement. In addition, automating the gas charging

using automatic valves could be made by introducing new variables to the fuzzy controller to

control the gas amount charged into the fridge

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10.0 REFERENCE

1. Michael Negnevitsky, Artificial Intelligence, Second Edition, Addison Wesley

2. Timoty J. Ross, fuzzy Logic With Engineering Application, Third edition, Wiley