New learning factor and testing methods for conjugate gradient training algorithm
Tae Kim, MICHAEL T. MANRY, Jeronimo Carvajal Maldonado · 2004
The conjugate gradient method has advantages over backpropagation in the training of artificial neural networks. Unlike previous investigators who have obtained learning factors using computationally expensive iterative line searches, we obtain the optimal learning factor in one step. We validate the learning factor with several tests, and analyze the input bias problem. Examples confirm the usefulness of improved conjugate gradient.