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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2008: Artificial Intelligence and Soft Computing – ICAISC 2008 pp 154–163Cite as

Robust MCD-Based Backpropagation Learning Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

Abstract

Training data containing outliers are often a problem for supervised neural networks learning methods that may not always come up with acceptable performance. In this paper a new, robust to outliers learning algorithm, employing the concept of initial data analysis by the MCD (minimum covariance determinant) estimator, is proposed. Results of implementation and simulation of nets trained with the new algorithm and the traditional backpropagation (BP) algorithm and robust Lmls are presented and compared. The better performance and robustness against outliers for the new method are demonstrated.

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Author information

Authors and Affiliations

  1. Wroclaw University of Technology, Wroclaw, Poland

    Andrzej Rusiecki

Authors
  1. Andrzej Rusiecki
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Editor information

Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

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© 2008 Springer-Verlag Berlin Heidelberg

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Rusiecki, A. (2008). Robust MCD-Based Backpropagation Learning Algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_16

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  • DOI: https://doi.org/10.1007/978-3-540-69731-2_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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