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Identification of the Linear Dynamic Parts of Wiener Model Using Kernel and Linear Adaptive

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Advanced Intelligent Systems for Sustainable Development (AI2SD’2020) (AI2SD 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1418))

Abstract

Kernel-based methods have had hefty success in a wide range of fields over the past decade, they are founded on the robust mathematical framework of reproducing kernel Hilbert spaces (RKHS), this space provides an interesting framework for the development of adaptive nonlinear filters. In this paper, we present a comparative study between the kernel method in Hilbert space with a reproducing kernel, and linear adaptive algorithms that is least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS) algorithms. Simulation results show excellent performance of the kernel algorithm for identification of single-input single-output (SISO) systems, compared to the linear adaptive algorithms, this by adopting the very fast fading channels called Broadband Radio Access Network (BRAN A and BRAN C).

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Authors and Affiliations

  1. Laboratory of Innovation in Mathematics, Applications and Information Technologies, Polydisciplinary Faculty, Sultan Moulay Slimane University, Po. Box 592, 23000, Beni Mellal, Morocco

    Rachid Fateh, Anouar Darif & Said Safi

Authors
  1. Rachid Fateh
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  2. Anouar Darif
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  3. Said Safi
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Corresponding author

Correspondence to Rachid Fateh .

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Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warszawa, Poland

    Janusz Kacprzyk

  2. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

  3. Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

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Fateh, R., Darif, A., Safi, S. (2022). Identification of the Linear Dynamic Parts of Wiener Model Using Kernel and Linear Adaptive. In: Kacprzyk, J., Balas, V.E., Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). AI2SD 2020. Advances in Intelligent Systems and Computing, vol 1418. Springer, Cham. https://doi.org/10.1007/978-3-030-90639-9_31

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