GENERALIZATION ABILITY OF FEEDFORWARD NEURAL NETWORK TRAINED BY FAHLMAN AND LEBIERE LEARNING ALGORITHM

Fahlman and Lebiere's (FL) learning algorithm begins with a two-layer network and in course of training, can construct various network architectures. We applied FL algorithm to the same three-layer network architecture as a back propagation (BP) network and compared their generalization properties. Simulation results show that FL algorithm yields excellent saturation of hidden units which can not be achieved by BP algorithm and furthermore, has more desirable generalization ability than that of BP algorithm.