Identification and application of bounded-parameter models
Automatica (Journal of IFAC)
IEEE Transactions on Information Theory
Estimation of parameter bounds from bounded-error data: a survey
Mathematics and Computers in Simulation - Parameter identifications with error bound
Parameter estimation algorithms for a set-membership description of uncertainty
Automatica (Journal of IFAC)
Optimal estimation theory for dynamic systems with set membership uncertainty: an overview
Automatica (Journal of IFAC)
On the value of information in system identification-Bounded noise case
Automatica (Journal of IFAC)
A Robust High-Order Mixed L2-Linfty Estimation for Linear-in-the-Parameters Models
Journal of Scientific Computing
Automation and Remote Control
Brief Paper: Convergence Properties of the Membership Set
Automatica (Journal of IFAC)
Brief Variable gain parameter estimation algorithms for fast tracking and smooth steady state
Automatica (Journal of IFAC)
Guaranteed non-asymptotic confidence regions in system identification
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
Hi-index | 22.15 |
This paper deals with some issues involving a parameter estimation approach that yields estimates consistent with the data and the given a priori information. The first part of the paper deals with the relationships between various noise models and the 'size' of the resulting membership set, the set of parameter estimates consistent with the data and the a priori information. When there is some flexibility about the choice of the noise model, this analysis can be helpful for noise model selection so that the resulting membership set yields a better estimate of the unknown parameter. The second part of the paper presents algorithms for various commonly encountered noise models that have the following properties: (a) they are recursive and easy to implement; and (b) after a finite 'learning period', the estimates provided by these algorithms are guaranteed to be in (or very 'close' to) the membership set. In general, the interpolatory algorithms, that produce an estimate in the membership set, do not possess nice statistical and worst-case properties similar to those of classical approaches such as least mean squares (LMS) and least squares (LS) algorithms. In the third part of the paper, we propose an algorithm that is optimal in a certain worst-case sense but gives an estimate that is in (or is 'close' to) the membership set.