A novel neural learning algorithm for multilayer perceptrons

Multilayer perceptron networks have been used to perform a variety of forecasting tasks, and back propagation is one of the most widely used training methods. It is a gradient method that can get stuck in local minima and has slow convergence. This paper presents a novel learning algorithm using the multiplier method. Testing results show that the new method has better convergence performance and generalization capability as compared to the back propagation method