OE-11.5 Fuzzy Logic System Components
You can build a fuzzy inference system end to end - fuzzification, membership assignment, rule base and decision making, then defuzzification - and compare defuzzification methods.
This unit assembles the previous one into a working controller: crisp input in, fuzzy inference through a rule base written in near-plain language, crisp output out. The rule base is the system's knowledge and it is usually written by a domain expert rather than learned, which is fuzzy control's great strength and its great limitation. Defuzzification methods differ in what they optimize - centroid is the common default because it uses the whole output set rather than a single point.
Work through these
Fuzzification
Turning a crisp measurement into degrees of membership, which is the first block of any such system. It is where the membership functions from the previous topic are used.
Membership value assignment
Deciding what degree a particular value gets, by method rather than by intuition. Several approaches exist and they disagree, which is a useful thing to know.
Development of the rule base
Writing the rules, which is where the expert knowledge actually lives. This is the item that makes these systems explainable in a way networks are not.
The decision-making system and fuzzy inference
Combining the rules that fired to produce a fuzzy answer. The inference method is a design choice with visible effects on the output surface.
Defuzzification to crisp sets
Turning that fuzzy answer back into a single number, because an actuator needs one. It is the last block and the one with the most competing methods.
Defuzzification methods and their comparison
The methods themselves, compared. Centroid is the default and the comparison shows you when it is the wrong default.
Applications of fuzzy logic systems
Where these systems are used, which is heavily in control. It closes the course by connecting the fuzzy half back to the kind of problem the network half also solves.
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