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• W2904342893 endingPage "61" @default.
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• W2904342893 abstract "Bilinear systems are considered as a particular class of nonlinear systems including the state variables which are typically used for online identification. By using a recursive identification method and the maximum likelihood principle, this paper presents two recursive-based algorithms to identify the parameters of bilinear in parameter systems with ARMA noise. In this regard, recursive generalized extended least squares (RGELS) and recursive Maximum Likelihood (RML) algorithms have been proposed for identification of bilinear systems. These algorithms can be used as an alternative choice in system identification with acceptable performance. The proposed algorithms estimate the correlated noise parameters with high accuracy by making full use of the measurement data. Simulation results indicate that the proposed algorithms are effective for online identification of bilinear in parameter systems with high convergence speed." @default.
• W2904342893 created "2018-12-22" @default.
• W2904342893 creator A5007815503 @default.
• W2904342893 creator A5052102472 @default.
• W2904342893 date "2019-05-01" @default.
• W2904342893 modified "2023-09-27" @default.
• W2904342893 title "Recursive generalized extended least squares and RML algorithms for identification of bilinear systems with ARMA noise" @default.
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• W2904342893 doi "https://doi.org/10.1016/j.isatra.2018.12.015" @default.
• W2904342893 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30580882" @default.
• W2904342893 hasPublicationYear "2019" @default.
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