Identification of non-linear systems by recursive kernel regression estimates

DOI10.1080/00207729308949508zbMath0769.93027OpenAlexW1979882024MaRDI QIDQ4696411

Adam Krzyżak

Publication date: 29 June 1993

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207729308949508

zbMATH Keywords

Hammerstein modelrandom noise

Mathematics Subject Classification ID

System identification (93B30) Linear systems in control theory (93C05)

Related Items (1)

Identificaction of nonlinear block-oriented systems by the recursive kernel estimate

Cites Work

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• Optimal global rates of convergence for nonparametric regression
• Weighted sums of certain dependent random variables
• Almost everywhere convergence of a recursive regression function estimate and classification (Corresp.)
• On the design of nonlinear discrete-time predictors (Corresp.)
• Identification of discrete Hammerstein systems using kernel regression estimates
• Signal identification after noisy nonlinear transformations
• Non-linear system identification using the Hammerstein model
• An iterative method for the identification of nonlinear systems using a Uryson model
• The rates of convergence of kernel regression estimates and classification rules

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