In theory, this algorithm tends to provide good generalization performance at extremely fast learning

speed.-The experimental results based on a few artificial and real benchmark function approximation and classification problems

including very large complex applications show that the new algorithm can produce good generalization performance in most cases and

can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks