Avaliação de Desempenho de Equalizador Neural Utilizando Algoritmo Genético
(Neural Equalizer Performance Evaluation Using Genetic Algorithm)
Tiago Andrade Mota (email@example.com)2, Jorgean Ferreira Leal (firstname.lastname@example.org)1, Antonio Cezar de Castro Lima (email@example.com)2
1Agência Nacional de Telecomunicações2Universidade Federal da Bahia
This paper appears in: Revista IEEE América Latina
Publication Date: Oct. 2015
Volume: 13, Issue: 10
Artificial Neural Networks (ANN) have been successfully applied to deal with linear or nonlinear problems. The best ANN architecture choice is not a trivial task to be performed and requires some a priori knowledge. In this work, we propose a Genetic Algorithm (GA) evaluation approach to determine the best combination of ANN and learning algorithm for equalization propose. A comparative analysis, using well known neural architectures, is presented in order to accomplish a 4-QAM equalization of signals submitted to Inter Symbol Interference (ISI), inherent in typical mobile communication channels. MLP, FLANN, PPN and three RNN based ANN structures, trained using backpropagation algorithm and others, have been evaluated.
Artificial Neural Network, Genetic Algorithm, Multipath Channel, Equalizer
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