FuzzyNeuronsFuzzy model of artificial neuron can be constructed byusing fuzzy operations at single neuron levely= g(w.x)W(X1,X2,...Xn)x=w=(Wi,W2,...Wn)
Fuzzy Neurons ◼ Fuzzy model of artificial neuron can be constructed by using fuzzy operations at single neuron level x = (x1,x2,. xn) w = (w1,w2,. wn) y= g(w.x)
FuzzyNeuronsy = g(w.x)y = g(A(w,x))Instead of weighted sum of inputs, more generalaggregation function is usedFuzzy union, fuzzy intersection and, more generallys-norms and t-norms can be used as an aggregationfunction for the weighted input to an artificial neuron
Fuzzy Neurons y = g(w.x) y = g(A(w,x)) ◼ Instead of weighted sum of inputs, more general aggregation function is used ◼ Fuzzy union, fuzzy intersection and, more generally, s-norms and t-norms can be used as an aggregation function for the weighted input to an artificial neuron
OR Fuzzy NeuronX,ANDWOR:[0,1]x[0,1]n->[0,1]X,ANDW,ORyX,ANDWny=OR(X1 AND W1, X2 AND W2 .. Xn AND W,)Transfer function g is linear If wk=0 then Wk AND Xk=0 while if Wk=1 then WkAND Xk= Xk independent of xk
OR Fuzzy Neuron ◼ Transfer function g is linear ◼ If wk=0 then wk AND xk=0 while if wk=1 then wk AND xk= xk independent of xk y=OR(x1 AND w1 , x2 AND w2 . xn AND wn) OR:[0,1]x[0,1]n->[0,1]