An information theoretic methodology for prestructuring neural networks

Absence of a priori knowledge about a problem domain typically forces use of overly complex neural network structures. An information-theoretic method based on calculating information transmission is applied to training data to obtain a priori knowledge that is useful for prestructuring (reducing complexity) of neural networks. The method is applied to a continuous system, and it is shown that such prestructuring reduces training time, and enhances generalization capability