A method of learning for multi-layer networks
A method of learning for multi-layer artificial neural networks is proposed. The learning model is designed to provide ail effective means of escape from the Backpropagation local minima. The system is shown to escape from the Backpropagation local minima and be of much faster convergence than simulated annealing techniques by simulations on the exclusive- or problem and the Arabic, numerals recognition problem.
multi-layer artificial neural networks backpropagation local minima temperature parameter gradient ascent