Fast recursive identification of state space models via exploitation of displacement structure
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Asymptotic convergence analysis of the projection approximation subspace tracking algorithms
Signal Processing - Special issue on subspace methods, part I: array signal processing and subspace computations
A linear regression approach to state-space subspace system identification
Signal Processing - Special issue: subspace methods, part II: system identification
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
Instrumental variable subspace tracking using projectionapproximation
IEEE Transactions on Signal Processing
On Consistency of Subspace Methods for System Identification
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Hierarchical gradient-based identification of multivariable discrete-time systems
Automatica (Journal of IFAC)
Hi-index | 22.15 |
Sometimes we obtain some prior information about a system to be identified, e.g., the order, model structure etc. In this paper, we consider the case where the order of a MIMO system to be identified is a priori known. Recursive subspace state-space system identification algorithms presented here are based on the gradient type subspace tracking method used in the array signal processing. The algorithms enable us to estimate directly the subspace spanned by the column vectors of the extended observability matrix of the system to be identified without performing the singular value decomposition. Also, a new convergence proof of the gradient type subspace tracking is given in this paper. Under the condition of a step size between 0 and 1, we prove the convergence property of the recursive equation of the gradient type subspace tracking. A numerical example illustrates that our algorithm is more robust with respect to the choice of the initial values than the corresponding PAST one.