Parametric and nonparametric curve fitting
From MaRDI portal
Publication:880378
DOI10.1016/j.automatica.2006.05.024zbMath1120.65013OpenAlexW2085944542MaRDI QIDQ880378
Carlo Novara, Kenneth Hsu, Mario Milanese, Tyrone L. Vincent, Kameshwar R. Poolla
Publication date: 15 May 2007
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2006.05.024
Numerical smoothing, curve fitting (65D10) Computer-aided design (modeling of curves and surfaces) (65D17)
Related Items
Data-driven design of two degree-of-freedom nonlinear controllers: the \(\operatorname{D}^2\)-IBC approach ⋮ Design of experiments for nonlinear system identification: a set membership approach ⋮ Unified set membership theory for identification, prediction and filtering of nonlinear systems ⋮ Control of MIMO nonlinear systems: a data-driven model inversion approach ⋮ Computation of local radius of information in SM-IBC identification of nonlinear systems ⋮ Sparse set membership identification of nonlinear functions and application to fault detection
Cites Work
- Unnamed Item
- Strong consistency of least squares estimates in normal linear regression
- Consistent nonparametric regression. Discussion
- Strong consistency of least squares estimates in dynamic models
- Recusrsive prediction error identification using the nonlinear Wiener model
- On global identifiability for arbitrary model parametrizations
- Nonlinear black-box modeling in system identification: A unified overview
- Nonlinear black-box models in system identification: Mathematical foundations
- Identification of nonlinear systems using empirical data and prior knowledge -- An optimization approach
- Some results on optimal experiment design
- Instrumental-variable methods for identification of Hammerstein systems
- Optimal input signals for parameter estimation in dynamic systems--Survey and new results
- Analysis of recursive stochastic algorithms
- Strong consistency of least squares estimates in multiple regression
- Convergence analysis of parametric identification methods