Myoelectric signal classification using evolutionary hybrid RBF-MLP networks

A. M S Zalzala, N. Chaiyaratana

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

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. The genetic and unsupervised learning algorithms are used to locate the centres of the RBF part in the hybrid network. In addition, the supervised learning algorithm, based on a back-propagation algorithm, is used to train the connection weights of the MLP part in the hybrid network. Performances of the hybrid network are initially tested using a two-spiral benchmark problem. Several simulation results are reported for applying the algorithm in the classification of myoelectric or electromyographic (EMG) signals where the GA-based network proved most efficient.

Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Evolutionary Computation, ICEC
Pages691-698
Number of pages8
Volume1
Publication statusPublished - 2000
Event2000 Congress on Evolutionary Computation - California, CA, USA
Duration: 16 Jul 200019 Jul 2000

Conference

Conference2000 Congress on Evolutionary Computation
Abbreviated title CEC 00
CityCalifornia, CA, USA
Period16/07/0019/07/00

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  • Cite this

    Zalzala, A. M. S., & Chaiyaratana, N. (2000). Myoelectric signal classification using evolutionary hybrid RBF-MLP networks. In Proceedings of the IEEE Conference on Evolutionary Computation, ICEC (Vol. 1, pp. 691-698)