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• W2321967718 abstract "In this paper, we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to nonlinear system identification have been shown to outperform regression methods under certain conditions, such as non-persistently exciting inputs and fast-sampling. We use the approximate Bayesian computation ABC algorithm to perform simulation-based inference of model parameters. The framework has the following main advantages: 1 parameter distributions are intrinsically generated, giving the user a clear description of uncertainty, 2 the simulation approach avoids the difficult problem of estimating signal derivatives as is common with other continuous-time methods, and 3 as noted above, the simulation approach improves identification under conditions of non-persistently exciting inputs and fast-sampling. Term selection is performed by judging parameter significance using parameter distributions that are intrinsically generated as part of the ABC procedure. The results from a numerical example demonstrate that the method performs well in noisy scenarios, especially in comparison to competing techniques that rely on signal derivative estimation." @default.
• W2321967718 created "2016-06-24" @default.
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• W2321967718 date "2015-10-12" @default.
• W2321967718 modified "2023-09-24" @default.
• W2321967718 title "Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation" @default.
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• W2321967718 doi "https://doi.org/10.1080/00207721.2015.1090643" @default.
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