Artificial Intelligence - Fuzzy Logic Systems (AI - Fuzzy Logic Systems) - MT4/MT5 Resources

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Artificial Intelligence - Fuzzy Logic Systems

sure

probably yes

can't say

Probably not

Of course not

1. It can control machines and consumer products.
2. It may not give accurate reasoning, but it can give acceptable reasoning.
3. Fuzzy logic helps deal with uncertainty in engineering.

Fuzzy logic system architecture
It has four main parts as shown in the figure-
fuzzification module − It converts the system input (clear numbers) into fuzzy sets. It divides the input signal into five steps such as -

LP

x is a large positive number

MP

x is a moderately positive value

S

x is very small

MN

x is a moderately negative value

LN

x is a large negative number

1. The x-axis represents the universe of discourse.
2. The y-axis represents the degree of membership in the interval [0, 1].

There can be multiple membership functions suitable for fuzzy values. Use simple membership functions because using complex functions does not increase the accuracy of the output.
All member functions LP, MP, S, MN, and LN are as follows-

The triangular membership function shape is most common among various other membership function shapes such as trapezoid, singleton, and Gaussian.
Here, the input to the 5-stage fuzzer varies from -10 volts to +10 volts. Therefore the corresponding output will also change.
Example of fuzzy logic system
Let us consider an air conditioning system with a 5-level fuzzy logic system. The system adjusts the temperature of the air conditioner by comparing the room temperature with the target temperature value.

algorithm

1. Define language variables and terms.
2. Construct membership functions for them.
3. Build a rule knowledge base.
4. Convert crisp data into fuzzy data sets using membership functions. (blurred)
5. Evaluate the rules in the rule base. (Interface Engine)
6. Combine the results of each rule. (Interface Engine)
7. Convert the output data to non-fuzzy values. (Deblurred)

logic development
Step 1: Define language variables and terms
Linguistic variables are input and output variables in the form of simple words or sentences. For room temperature, cold, warm, hot, etc. are all linguistic terms.
Temperature (t) = {very cold, cold, warm, very warm, hot}
Each member of the set is a linguistic term that covers a portion of the overall temperature value.

Step 2: Construct membership functions for them
The membership function of the temperature variable is as follows-

Step3: Build knowledge base rules
Create a matrix of room temperature values ​​versus the target temperature values ​​expected to be provided by the air conditioning system.

Step 4: Get the fuzzy value
Evaluation of execution rules for fuzzy set operations. The operations of OR and AND are Max and Min respectively. All evaluation results are combined to form the final result. The result is a fuzzy value.

Step 5: Perform defuzzification
Defuzzification is then performed based on the membership functions of the output variables.

Application areas of fuzzy logic
The key application areas of fuzzy logic are as follows-
Automotive systems

1. automatic transmission
2. four wheel steering
3. Vehicle environment control

consumer electronics

1. High fidelity system
2. copier
3. Still cameras and video cameras
4. television

Domestic goods

1. Micro-wave oven
2. refrigerator
3. toaster
4. Vacuum cleaner
5. washing machine

environmental control

1. Air conditioner/dryer/heater
2. humidifier

Advantages of FLS

1. The mathematical concepts in fuzzy inference are very simple.
2. Due to the flexibility of fuzzy logic, you can modify FLS simply by adding or removing rules.
3. Fuzzy logic systems can accept imprecise, distorted, and noisy input information.
4. FLS is easy to build and understand.
5. Fuzzy logic is a solution to complex problems in all areas of life, including medicine, because it resembles human reasoning and decision-making.

Disadvantages of FLS

1. There is no systematic approach to fuzzy system design.
2. They can only be understood in simple terms.
3. They are suitable for solving problems that do not require high accuracy.

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