Abstract
Robust neural network learning algorithms are often applied to deal with the problem of gross errors and outliers. Unfortunately, such methods suffer from high computational complexity, which makes them ineffective. In this paper, we propose a new robust learning algorithm based on the LMLS (Least Mean Log Squares) error criterion. It can be considered, as a good trade-off between robustness to outliers and learning efficiency. As it was experimentally demonstrated, the novel method is not only faster but also more robust than the LMLS algorithm. Results of implementation and simulation of nets trained with the new algorithm, the traditional backpropagation (BP) algorithm and robust LMLS method are presented and compared.
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Rusiecki, A. (2010). Fast Robust Learning Algorithm Dedicated to LMLS Criterion. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_13
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DOI: https://doi.org/10.1007/978-3-642-13232-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13231-5
Online ISBN: 978-3-642-13232-2
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