Chapter 2. Prehension

Appendix C - Computational Neural Modelling 393
4 Generalized
Figure C.6 The generalized delta rule is shown, where a teacher is
needed to compute the error between the desired output and actual
output.
Awij = (tpi - *i> 9j (13)
where the actual output Opi is subtracted from the desired output tpi for
a particular inputloutput pair, p. For the output layer of a layered
network, it is the same as the delta rule. For the other layers, the error,
E, is still dependent on the weights, but a continuation of the chain
rule must be applied to show the mathematical dependence. The error
is propagated backwards.
To perform a computation in an adaptive neural network using the
generalized delta rule, two phases must occur. At the start, weights are
initially random, or arbitrary, values. The training set is presented to
the network, one input/output pair p at a time. The first phase is a
forward computation using equation (3) to sum the products of the
inputs and weights to each neuron in order to produce an activation
value. Equation (4) is used to threshold the result. This computation
propagates forward for all neurons in the network. The results at the
output neurons are then compared to the desired outputs stored in the