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EnglishOne of the most versatile and powerful algorithms for the identification of nonlinear dynamical systems is the NARMAX (Nonlinear Auto-regressive Moving Average with eXogenous inputs) approach. The model represents the current output of a system by a nonlinear regression on past inputs and outputs and can also incorporate a nonlinear noise model in the most general case. In recent papers, one of the authors introduced a NARX (no noise model) formulation based on Gaussian Process (GP) regression and derived the corresponding expressions for Higher-order Frequency Response Functions (HFRFs). This paper extends the theory for the GP-NARX framework by providing a means of converting the GP prediction bounds in the time domain into bounds on the HFRFs. The approach is demonstrated on the Duffing oscillator.
Uncertainty Bounds on Higher-Order FRFs from Gaussian Process NARX Models / Worden, Keith; Surace, Cecilia; Becker, William. - In: PROCEDIA ENGINEERING. - ISSN 1877-7058. - ELETTRONICO. - 199(2017), pp. 1994-2000. ((Intervento presentato al convegno 10th International Conference on Structural Dynamics, EURODYN 2017 tenutosi a Faculty of Civil and Industrial Engineering, ita nel 2017.
| Titolo: | Uncertainty Bounds on Higher-Order FRFs from Gaussian Process NARX Models |
| Autori: | |
| Data di pubblicazione: | 2017 |
| Rivista: | |
| Abstract: | One of the most versatile and powerful algorithms for the identification of nonlinear dynamical s...ystems is the NARMAX (Nonlinear Auto-regressive Moving Average with eXogenous inputs) approach. The model represents the current output of a system by a nonlinear regression on past inputs and outputs and can also incorporate a nonlinear noise model in the most general case. In recent papers, one of the authors introduced a NARX (no noise model) formulation based on Gaussian Process (GP) regression and derived the corresponding expressions for Higher-order Frequency Response Functions (HFRFs). This paper extends the theory for the GP-NARX framework by providing a means of converting the GP prediction bounds in the time domain into bounds on the HFRFs. The approach is demonstrated on the Duffing oscillator. |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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