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On the Cesàro-Means-Based Orthogonal Series Approach to Learning Time-Varying Regression Functions

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Artificial Intelligence and Soft Computing (ICAISC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9693))

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Abstract

In this paper an incremental procedure for nonparametric learning of time-varying regression function is presented. The procedure is based on the Cesàro-means of orthogonal series. Its tracking properties are investigated and convergence in probability is shown. Numerical simulations are performed using the Fejer’s kernels of the Fourier orthogonal series.

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Acknowledgement

This work was supported by the Polish National Science Center under Grant No. 2014/15/B/ST7/05264.

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Authors and Affiliations

  1. The Institute of Computational Intelligence, Czestochowa University of Technology, Armii Krajowej 36, 42-200, Czestochowa, Poland

    Piotr Duda, Lena Pietruczuk & Maciej Jaworski

  2. Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada

    Adam Krzyzak

Authors
  1. Piotr Duda
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  2. Lena Pietruczuk
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  3. Maciej Jaworski
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  4. Adam Krzyzak
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Corresponding author

Correspondence to Piotr Duda .

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Editors and Affiliations

  1. Częstochowa University of Technology, Czestochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Czestochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Czestochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Duda, P., Pietruczuk, L., Jaworski, M., Krzyzak, A. (2016). On the Cesàro-Means-Based Orthogonal Series Approach to Learning Time-Varying Regression Functions. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_4

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  • DOI: https://doi.org/10.1007/978-3-319-39384-1_4

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