On-line neural training algorithm with sliding mode control and adaptive learning rate

Authors:
A. Nied;S. I. Seleme, Jr.;G. G. Parma;B. R. Menezes
Affiliations:
Department of Electrical Engineering, State University of Santa Catarina, Joinville, SC, Brazil;Department of Electronics Engineering, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil;Department of Electronics Engineering, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil;Department of Electronics Engineering, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
Venue:
Neurocomputing
Year:
2007

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Abstract

This paper presents a new algorithm for on-line artificial neural networks (ANN) training. The network topology is a standard multilayer perceptron (MLP) and the training algorithm is based on the theory of variable structure systems (VSS) and sliding mode control (SMC). The main feature of this novel procedure is the adaptability of the gain (learning rate), which is obtained from sliding mode surface so that system stability is guaranteed.