Computational experiments in training feedforward neural networks

摘要

This investigation of the supervised training of feedforward neural networks has two goals. The derivation of an efficient and reliable method and valid training. It discusses the drawbacks of current methods and improves on them by means of a global approach. Quartic random optimization and genetic algorithms both find good solutions for smaller metworks. When combined with steepest descent, the trained networks perform more robustly