where: Dwjk,s(t-1) is the last change in weight between nodes k and j at time t and segment s
dj,s is the error signal at node j and segment s
oj is the output at node j and segment s
This technique has the disadvantage of needing to store the input values of each pattern in the sequence, along with the different values of d for the hidden nodes. However, as this project will not be looking at particularly long sequences, this was not regarded as a major problem.
3.5. Kohonen Self-Organising Maps
The backpropagation techniques used in training MLPs rely on the classification type for each training pattern being known in advance. In as much this type of training is referred to as 'supervised learning'. Self-Organising Maps (SOMs) do not require this information during training and therefore undergo 'unsupervised learning'.