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Exact orthogonal kernel estimation from finite data records: Extending Wiener's identification of nonlinear systems

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Abstract

A technique is described for exact estimation of kernels in functional expansions for nonlinear systems. The technique operates by orthogonalizing over the data record and in so doing permits a wide variety of input excitation. In particular, the excitation is not limited to inputs that are white, Gaussian, or lengthy. Diagonal kernel values can be estimated, without modification, as accurately as off-diagonal values. Simulations are provided to demonstrate that the technique is more accurate than the Lee-Schetzen method with a white Gaussian input of limited duration, retaining its superiority when the system output is corrupted by noise.

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Authors and Affiliations

  1. Department of Electrical Engineering, Queen's University, K7L 3N6, Kingston, Ontario, Canada

    M. J. Korenberg & S. B. Bruder

  2. Northern Telecom Canada Limited, Brampton, Ontario, Canada

    P. J. McLlroy

Authors
  1. M. J. Korenberg
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  2. S. B. Bruder
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  3. P. J. McLlroy
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Korenberg, M.J., Bruder, S.B. & McLlroy, P.J. Exact orthogonal kernel estimation from finite data records: Extending Wiener's identification of nonlinear systems. Ann Biomed Eng 16, 201–214 (1988). https://doi.org/10.1007/BF02364581

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  • DOI: https://doi.org/10.1007/BF02364581

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