In a Neural Network there are two methods that we can train the network. One of method is Supervised Training. To do that Training we can use the Delta Rule. This is one of Example that I had found.
Can anybody explain me how to find the Error of this question?
This is how we explain the supervised training of the Neural Network
Supervised Training
Training Data Set and Test Data Set are selected from Past or Historical Data.
Targets are selected
Weights are initially set randomly.
Input is applied and the Network Output is determined
Error = Target – Network Output is calculated
Weights are updated until Error à 0
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This is the theoretical explanation of the Delta rule
Supervised Training – Delta Rule
Dw = hdx
Dw = wnew – wold
h = Learning Rate Parameter
d = Error (= Target – Network Output)
x = Input Vector
wnew – wold= hdx
wnew = hdx + wold
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Let’s see the example that has given,
Lets assume the Target Is=1
So Network output, Y
Y = 1 x 0.3 + 0.5 x 0.4 + 0.3 x 0.5 = 0.65
Then we can find the Error
Error d = Target – Network Output = 1– 0.65 = 0.35
