Abstract
In the paper a three-stage, semirecursive scheme for the Wiener-Hammerstein system identification is proposed. The algorithm combines both parametric and nonparametric strategies and allows to recover linear and nonlinear subsystems directly from the noisy inputoutput data. As to the nonlinearity, the main idea is based on the recursive kernel censoring of measurements, while linear dynamics are recovered by a special kind of deconvolution. Efficiency of the obtained estimates is justified by numerical example.
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Hasiewicz, Z., Wachel, P., Mzyk, G., Kozdraś, B. (2017). Multistage identification of Wiener-Hammerstein system. In: Mitkowski, W., Kacprzyk, J., Oprzędkiewicz, K., Skruch, P. (eds) Trends in Advanced Intelligent Control, Optimization and Automation. KKA 2017. Advances in Intelligent Systems and Computing, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-319-60699-6_51
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DOI: https://doi.org/10.1007/978-3-319-60699-6_51
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