
In this chapter we consider self-organizing networks. The main difference between them and conventional models is that the correct output cannot be defined a priori, and therefore a numerical measure of the magnitude of the mapping error cannot be used. However, the learning process leads, as before, to the determination of well-defined network parameters for a given application.