Uncertainty quantification of input matrices and transfer function in input/output subspace system identification

Subspace system identification

We give a general overview of the state-of-the-art in subspace system identification methods. We have restricted ourselves to the most important ideas and developments since the methods appeared in the late eighties. First, the basis of linear subspace identification are summarized. Different algorithms one finds in literature (Such as N4SID, MOESP, CVA) are discussed and put into a unifyin...

Uncertainty Quantification for Stochastic Subspace Identification of Multi-Setup Measurements

In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios and mode shapes) obtained from Stochastic Subspace Identification (SSI) of a structure, are afflicted with statistical uncertainty. For evaluating the quality of the obtained results it is essential to know the appropriate confidence intervals of these figures. In this paper we present algorithms that autom...

Uncertainty Quantification for Eigensystem-Realization-Algorithm, a Class of Subspace System Identification

In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios and mode shapes), obtained from Stochastic Subspace Identification of structures, are subject to statistical uncertainty from ambient vibration measurements. It is hence neccessary to evaluate the confidence intervals of these obtained results. This paper will propose an algorithm that can efficiently estim...

Identification of Multiple - Input Transfer Function Models

This paper proposes a procedure for transfer function identification (specification) based on least-squares estimates of transfer function weights using the original or filtered series. The corner method is then used to identify a parsimonious rational form of the transfer function. The procedure is illustrated in a simulated example; it is shown how this straightforward approach outperforms ot...

MIMO system identification: state-space and subspace approximations versus transfer function and instrumental variables