Third, find out how large the error is on the output unit. Fourth, use one of the back-propagation formulas to adjust the weights leading into the output unit. The idea is to try to make the answer come out just a little bit closer to the right answer. Fifth, use another formula to find out errors for the hidden layer unit. Sixth, adjust the weights leading into the hidden layer unit via another formula.
Repeat steps one thru six for the second, third and fourth xor patterns.
Even after all these changes to the weights the answers will only be a little closer to the right answers and the whole process has to be repeated many times. Each time all the patterns in the problem have been used once we will call that an iteration although often other people call this an epoch . We will now look at the formulas for adjusting the weights that lead into the output units of a back-propagation network. The actual activation value of an output unit, k, will be o k and the target for unit, k, will be t k . The k subscript is for all the units in the output layer however in this example there is only one unit.