An important focus of neural network research is the question of how to adjust the weights of the links to get the desired system behavior. This modification is very often based on the Hebbian rule, which states that a link between two units is strengthened if both units are active at the same time. The Hebbian rule in its general form is:
where:
Training a feed-forward neural network with supervised learning consists of the following procedure:
An input pattern is presented to the network. The input is then propagated forward in the net until activation reaches the output layer. This constitutes the so called forward propagation phase.
The output of the output layer is then compared with the teaching
input. The error, i.e. the difference (delta) 
between the
output 
and the teaching input 
of a target output unit
j is then used together with the output 
of the source unit
i to compute the necessary changes of the link 
. To compute
the deltas of inner units for which no teaching input is available,
(units of hidden layers) the deltas of the following layer, which are
already computed, are used in a formula given below. In this way the
errors (deltas) are propagated backward, so this phase is called
backward propagation.
In online learning, the weight changes 
are
applied to the network after each training pattern, i.e. after each
forward and backward pass. In offline learning or batch
learning the weight changes are cumulated for all patterns in the
training file and the sum of all changes is applied after one full
cycle (epoch) through the training pattern file.
The most famous learning algorithm which works in the manner described is currently backpropagation. In the backpropagation learning algorithm online training is usually significantly faster than batch training, especially in the case of large training sets with many similar training examples.
The backpropagation weight update rule, also called generalized delta-rule reads as follows:
where:
There are several backpropagation algorithms supplied with SNNS: one
``vanilla backpropagation'' called Std_Backpropagation, one
with momentum term and flat spot elimination called
BackpropMomentum and a batch version called BackpropBatch. They
can be chosen from the remote panel with the button 
and the menu selection select learning function.
In SNNS, one may either set the number of training cycles in advance or train the network until it has reached a predefined error on the training set.