A comparative evaluation of sequential constructive methods

A general treatment of a particular class of learning techniques for neural
networks, called sequential constructive methods, is proposed. They
subsequently add units to the hidden layer until all the input-output
relations contained in a given training set are satisfied.
Every addition involves the update of a small portion of the whole
weight matrix and depends on a subset of samples whose size decreases
with time. In most cases this leads to a large reduction of the computational
cost.
General convergence theorems are presented that ensure the achievement
of a good multilayer perceptron within a finite execution time. The
output weights need not to be trained but are obtained by the application
of simple algebraic equations.