A simple procedure in back-propagation training

The standard back-propagation (BP) algorithm for multilayer feedforward neural networks is basically a gradient-descent method, it has the problems of local minima and slow convergence. In this paper, a simple method based on the BP algorithm by employing an adaptive learning rate and momentum factor to reduce the training time is presented. Simulation results indicate a superior convergence speed as compared to other competing methods.