Let us consider an air conditioning system with 5-level fuzzy logic system. This system adjusts the temperature of air conditioner by comparing the room temperature and the target temperature value.
Algorithm
● Define linguistic variables and terms.
● Construct membership functions for them.
● Construct knowledge base of rules.
● Convert crisp data into fuzzy data sets using membership functions. (fuzzification)
● Evaluate rules in the rule base. (Inference Engine)
● Combine results from each rule. (Inference Engine)
● Convert output data into non-fuzzy values. (defuzzification)
Logic Development
Step 1: Define linguistic 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 linguistic terms.
Temperature (t) = {very-cold, cold, warm, very-warm, hot}
Every member of this set is a linguistic term and it can cover some portion of overall temperature values.
Step 2: Construct membership functions for them
The membership functions of temperature variable are as shown −
Step3: Construct knowledge base rules
Create a matrix of room temperature values versus target temperature values that an air conditioning system is expected to provide.
| RoomTemp. /Target | Very_Cold | Cold | Warm | Hot | Very_Hot |
| Very_Cold | No_Change | Heat | Heat | Heat | Heat |
| Cold | Cool | No_Change | Heat | Heat | Heat |
| Warm | Cool | Cool | No_Change | Heat | Heat |
| Hot | Cool | Cool | Cool | No_Change | Heat |
| Very_Hot | Cool | Cool | Cool | Cool | No_Change |
Build a set of rules into the knowledge base in the form of IF-THEN-ELSE structures.
| Sr. No. | Condition | Action |
| 1 | IF temperature=(Cold OR Very_Cold) AND target=Warm THEN | Heat |
| 2 | IF temperature=(Hot OR Very_Hot) AND target=Warm THEN | Cool |
| 3 | IF (temperature=Warm) AND (target=Warm) THEN | No_Change |
Step 4: Obtain fuzzy value
Fuzzy set operations perform evaluation of rules. The operations used for OR and AND are Max and Min respectively. Combine all results of evaluation to form a final result. This result is a fuzzy value.
Step 5: Perform defuzzification
Defuzzification is then performed according to membership function for output variable.
