Redes neurais construtivas para a classificação de padrões.
Abstract
Constructive neural learning is a neural learning model that does not assume a fixed
topology before training begins. The main characteristic of this learning model is the
dynamic construction of the network hidden layers which occurs simultaneously with training.
This research work investigates six constructive neural algorithms namely, tower, pyramid, tiling, upstart, Distal and cascade-correlation, evaluating each of them with relation to advantages and disadvantages, ease of training, size and topology of the network, restrictions and performance. The work presents a computational system (CONEB) which implements each algorithm. Results obtained by using the different algorithms in several knowledge domains
are presented and analysed.