Performance Evaluation of a New BP Algorithm for a Modified Artificial Neural Network

摘要

In a conventional artificial neural model, the nonlinear activation function (AF) follows the weight sum operation. In this paper, the AF is placed before the connecting weights of each artificial neuron and hence modified artificial neural network (MANN) is proposed and the corresponding backpropagation (BP) learning algorithm is derived. Further, the slope of AF which is conventionally fixed during the training phase is adjusted for achieving better and faster training of the multi-layer artificial neural network (MANN). In this case, also both the weights and slope update algorithms are derived. To assess and compare the performance of these two ANN models, standard applications such as classification, nonlinear system identification (direct modeling), nonlinear channel equalization (inverse modeling) are implemented through simulation and compared with that obtained by conventional BP based MANN. The simulation results demonstrate that the adaptive slope based MANN outperforms the fixed slope as well as conventional MANN in terms of three different performance measures such as the number of iterations to converge, mean of the square of residual error and mean absolute deviation.