Step 1 : Set output layer weights and learing rate
w <- [0.5, 0.5, 0.5]
a <- (0 <= a <= 1)
Step 2 : Initliaze hidden later weights and counter
v <- small random number
p <- number of training pattern pairs
k <- 1
Step 3 : For each training pattern pair (x, d),
do Step 4 - 7 until k = p
Step 4 : Compute hidden layer outputs
z = +1 ; NET_z >= 0
-1 ; NET_z < 0
Step 5 : Compute output
y = +1 ; NET_y >= 0
-1 ; NET_y < 0
Step 6 : Compare output and desired output
If y = d, k <- k + 1 and goto Step 3
else Update hidden layer weights
If d = 1 update weights to z whose NET_z is closest to zero
delta_v = a * (d - NET_z) * x
v = v + delta_v
If d = -1 update wegiths to z whose NET_z is positive
delta_v = a * (d - NET_z) * x
v = v + delta_v
Setp 7 : Increase counter and goto Step 3
k <- k + 1
Step 8 : Test stop condition
If maximum of delta_v < tolerance in Step 4-7, stop
else, k <- 1 and goto Step 3
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아오 여기글보다가 교수님이 하라고 던져준 neural network using c-sharp 어쩌구 책이 갑자기 생각이났넥,,