IMPROVEMENTS OF BACK PROPAGATION ALGORITHM PERFORMANCE BYADAPTIVELY CHANGING GAIN, MOMENTUM AND LEARNING RATE
Norhamreeza Abdul Hamid, Rozaida Ghazali Nazri Mohd Nawi · International Journal of New Computer Architectures and their Applications · 2011
In some practical Neural Network (NN) applications, fast response to external events within enormously short time is highly demanded. However, by using back propagation (BP) based on gradient descent optimisation method obviously not satisfy in several application due to serious problems associated with BP which are slow learning convergence velocity and confinement to shallow minima. Over the years, many improvements and modifications of the BP learning algorithm have been reported. In this research, we modified existing BP learning algorithm with adaptive gain by adaptively change the momentum coefficient and learning rate. In learning the patterns, the simulation results indicate that the proposed algorithm can hasten up the convergence behaviour as well as slide the network through shallow local minima compare to conventional BP algorithm. We use five common benchmark classification problems to illustrate the improvement of the proposed algorithm.