Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
A bibliography on nonlinear system identification
Signal Processing - Special section on digital signal processing for multimedia communications and services
On the Global Convergence of a Filter--SQP Algorithm
SIAM Journal on Optimization
On the Role of Natural Level Functions to Achieve Global Convergence for Damped Newton Methods
Proceedings of the 19th IFIP TC7 Conference on System Modelling and Optimization: Methods, Theory and Applications
Frequency-domain subspace system identification using non-parametric noise models
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Maximum likelihood estimation of Gaussian models with missing data-Eight equivalent formulations
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Hi-index | 22.15 |
In this paper, two nonlinear optimization methods for the identification of nonlinear systems are compared. Both methods estimate the parameters of e.g. a polynomial nonlinear state-space model by means of a nonlinear least-squares optimization of the same cost function. While the first method does not estimate the states explicitly, the second method estimates both states and parameters adding an extra constraint equation. Both methods are introduced and their similarities and differences are discussed utilizing simulation data. The unconstrained method appears to be faster and more memory efficient, but the constrained method has a significant advantage as well: it is robust for unstable systems of which bounded input-output data can be measured (e.g. a system captured in a stabilizing feedback loop). Both methods have successfully been applied on real-life measurement data.