In this paper we consider a unified framework for parameter estimation problems. Under this framework, the unknown parameters appear in a linear fractional transformation (LFT). A key advantage of the LFT problem formulation is that it allows us to efficiently compute gradients, Hessians, and Gauss-Newton directions for general parameter estimation problems without resorting to inefficient finite-difference approximations. The generality of this approach also allows us to consider issues such as identifiability, persistence of excitation, and convergence for a large class of model structures under a single unified framework.
- Linear fractional transformation
- Maximum likelihood
- Parameter estimation
- System identification
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering