So now using our Inference Engine, the Engine that would compare and deduct our fuzzy output, with fuzzy input ( 0.8 Low, 0.4 High) we can say based upon the rules mentioned above that the brakes will be down by (0.4 Pushed 0.8 Released) -> Fuzzy output.
Rule 2: If Speed is Low, Then Brake is Released Rule 1: If Speed is High, Then Brake is Pushed A rule base is a set of rules that is responsible for final output. Now for a singleton input of 4, the fuzzy value = y = (4-0)/(5-0) = 0.8, therefore our fuzzy input = 0.8 Low, imagine that the linguistic variables are intersecting so that a single input can be defined as (0.8 Low 0.4 High).Īfter we get the fuzzy inputs, we compare it against a rule base. Imagine more if the range from 0-10 is not constant but a triangular graph with the max height of 1 and for Low a triangle graph with 3 distinct points 0,5,10. So If I got a Crisp input of 5 -> its fuzzy value is Low. I have a train, I considered the Linguistic Variable "Speed" of the train has 2 membership functions Low and High, in which "Low" has range from 0 mph - 10 mph and "High" from 10 mph - 20 mph. Fuzzy logic is an approximation process, in which crisp inputs are turned to fuzzy values based on linguistic variables, set of rules and the inference engine provided. This article is about a fuzzy logic controller based on mamdani Inference Engine. Download FuzzyLogicController DLL including Gaussian Membership Function gesture from Cigdem "Thank you".
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