Title: An improved back propagation algorithm for training neural network-based equaliser for signal restoration in digital communication channels
Authors: Zohra Zerdoumi; Djamel Chikouche; Djamel Benatia
Addresses: Electronic Department, Batna University, Avenue Chahid Boukhlouf 05000, Algeria; Electronic Department, M'sila University, BP 166 Ichbilia 28000, Algeria ' Electronic Department, M'sila University, BP 166 Ichbilia 28000, Algeria ' Electronic Department, Batna University, Avenue Chahid Boukhlouf 05000, Algeria
Abstract: The back propagation (BP) algorithm has been very successful in training multilayer perceptron-based equalisers; despite its success BP convergence is still too slow. Within this paper we present a new approach to enhance the training efficiency of the multilayer perceptron-based equaliser (MLPE). Our approach consists on modifying the conventional back propagation algorithm, through creating an adaptive nonlinearity in the activation function. Experiment results evaluates the performance of the MLPE trained using the conventional BP and the improved back propagation with adaptive gain (IBPAG). Due to the adaptability of the activation function gain the nonlinear capacity and flexibility of the MLP is enhanced significantly. Therefore, the convergence properties of the proposed algorithm are more improved compared to the BP. The proposed algorithm achieves the best performance in the entire simulation experiments.
Keywords: channel equalisers; artificial neural networks; ANNs; digital communication channels; signal restoration; back propagation; adaptive activation function; training efficiency; multilayer perceptron-based equalisers; MLPE; simulation.
DOI: 10.1504/IJMNDI.2016.081666
International Journal of Mobile Network Design and Innovation, 2016 Vol.6 No.4, pp.236 - 244
Received: 14 Apr 2016
Accepted: 13 May 2016
Published online: 19 Jan 2017 *