Fuzzy Neural Network Tutorial - UNR

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Fuzzy Neural Network TutorialFuzzy Neural NetworksOur Fuzzy Neural networks (FNN s) are similar to the PNN s. Let there be K classes and letx be any feature vector from the population of interest to be recognized. The Class k exemplarfeature vectors are denoted by x for q(k) = 1,...,Q(k). The summed functions here are not scaledk())q(and have a maximum value of (q1=1,Q(1))1f(x) = (1/Q(1))E exp{-2x - x2/(2F)} (1)()22q : :K(q(K)=1,Q(K)Kf(x) = (1/Q(K))G exp{-2x - x2/(2F )} (1)K)()22q(How the FNN WorksThe summed functions in Equation (1) are averages of values between 0 and 1 and so are12between 0 and 1. Fuzzy logic uses truth values between 0 and 1, so the output values f(x) and f(x)are the Fuzzy truths that the input vector belongs to Class 1 and Class 2, respectively. We say thefuzzy truths are the values of Fuzzy set membership functions whose functional values are the fuzzytruths of memberships each of the classes.)

Fuzzy Neural Network Tutorial Fuzzy Neural Networks Our fuzzy neural networks (FNN’s) are similar to the PNN’s. Let there be K classes and le t ... Fuzzy logic uses truth values between 0 and 1, so the output values f 12 (x) and f (x) are the fuzzy truths that the input vector belongs to Class 1 and Class 2, respectively. We say the

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