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Uncertainty Bounds on Higher-Order FRFs from Gaussian Process NARX Models.
- Source :
- Procedia Engineering; 2017, Vol. 199, p1994-2000, 7p
- Publication Year :
- 2017
-
Abstract
- One 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18777058
- Volume :
- 199
- Database :
- Supplemental Index
- Journal :
- Procedia Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 125100594
- Full Text :
- https://doi.org/10.1016/j.proeng.2017.09.317