Modified Back Propagation Algorithm of Feed Forward Networks

Abstract—The Back-propagation Neural Network (BPNN) Algorithm is widely used in solving many real time problems in world. It is highly suitable for the problems which involve large amount of data and there is no relationships found between the outputs and inputs. However BPNN possesses a problem of slow convergence and convergence to the local optimum. Over the years, many improvements and modifications of the BP learning algorithm have been reported to overcome these shortcomings. In this paper, a modified backpropagation algorithm (MBP) based on minimization of the sum of the squares of errors is proposed and implemented on benchmark XOR problem. Implementation results show that MBP outperforms standard backpropagation algorithm with respect to number of iterations and speed of connvergence