A method of improving generalization performance of BP network based on random assistant samples

Under the condition of the chosen structure of BP neural network, the underlying reason for the good accuracy in training and poor generalization performance in testing of BP network under the under-determined status is analyzed. A new BP network generalization learning algorithm based on suboptimal criterion of fitting error of random assistant samples is presented. Theoretic analysis and simulation results show that the method is practical and feasible.