Continuous-time approaches to system indentification—a survey
Automatica (Journal of IFAC) - Identification and system parameter estimation
Parameter estimation for continuous-time models-A survey
Automatica (Journal of IFAC)
Brief Applying the EKF to stochastic differential equations with level effects
Automatica (Journal of IFAC)
Optimal smoothing of non-linear dynamic systems via Monte Carlo Markov chains
Automatica (Journal of IFAC)
Stochastic modeling of film porosity in thin film deposition
ACC'09 Proceedings of the 2009 conference on American Control Conference
System identification of nonlinear state-space models
Automatica (Journal of IFAC)
Nonlinear gray-box identification using local models applied to industrial robots
Automatica (Journal of IFAC)
Input estimation in nonlinear dynamical systems using differential algebra techniques
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
An efficient and flexible parameter estimation scheme for grey-box models in the sense of discretely, partially observed Ito stochastic differential equations with measurement noise is presented along with a corresponding software implementation. The estimation scheme is based on the extended Kalman filter and features maximum likelihood as well as maximum a posteriori estimation on multiple independent data sets, including irregularly sampled data sets and data sets with occasional outliers and missing observations. The software implementation is compared to an existing software tool and proves to have better performance both in terms of quality of estimates for nonlinear systems with significant diffusion and in terms of reproducibility. In particular, the new tool provides more accurate and more consistent estimates of the parameters of the diffusion term.