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- W2467009449 abstract "This paper presents a new approach for the identification of linear state-space models from input–output measurements in the presence of noise. In contrast to the original OKID/ERA algorithm, which works through the observer Markov parameters, the new approach uses the estimated observer output residuals to convert a stochastic identification problem into a virtually deterministic one. A system state-space model and an associated Kalman-filter gain can be then identified. Because the converted problem is virtually deterministic, any deterministic system-identification method can be used. The proposed stochastic-to-deterministic conversion enables these deterministic algorithms to produce the same performance level as the existing stochastic system-identification algorithms." @default.
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- W2467009449 date "2017-12-01" @default.
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- W2467009449 title "OKID via Output Residuals: A Converter from Stochastic to Deterministic System Identification" @default.
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- W2467009449 doi "https://doi.org/10.2514/1.g001786" @default.
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