EMG Signal Classification using Evolutionary Networks

摘要

This paper introduces a hybrid neural structure using radial-basis function (RBF) and multilayer perceptron (MLP) networks. The hybrid network is composed of one RBF network and a number of MLPs, and is trained using a combined genetic/ unsupervised/ supervised learning algorithm. Genetic and unsupervised learning algorithms are used to locate centres of the RBF part in the hybrid network. In addition, supervised learning algorithm, based on back-propagation algorithm, is used to train connection weights of the MLP part in the hybrid network. Performance of the hybrid network is initially tested using the two-spiral benchmark problem. Several simulation results are reported for applying the algorithm in the classification of Electromyogram signals (EMG) where the genetic-based network proved most efficient.