Ombp: Optic Modified Backpropagation Training Algorithm For Fast Convergence Of Feedforward Neural Network

In this paper, we propose an algorithm for a fast training and accurate prediction for Feedforward Neural Network (FNN). In this algorithm OMBP, we combine Optic Backpropagation (OBP) with the Modified Backpropagation algorithm (MBP). The weights are initialized using Yam and Chow algorithm to insure the stability of OMBP and to reduce its sensitivity against initial settings. The proposed algorithm had shown an upper hand over three different algorithms in terms of number of iterations, time needed to reach the convergence and the accuracy of the prediction. We have tested the proposed algorithm on several benchmark data and compared its results with those obtained by applying the standard BackPropagation algorithm (SBP), Least Mean Fourth algorithm (LMF), and the Optic Backpropagation (OBP). The criterions used in the comparisons are: number of iterations, and time needed for convergence, the error of prediction, and percentage of trials failed to converge.