Comparison of some instrumental variable methods-Consistency and accuracy aspects
Automatica (Journal of IFAC)
Brief paper: On the uniqueness of maximum likelihood identification
Automatica (Journal of IFAC)
Paper: Recursive output error identification algorithms theory and evaluation
Automatica (Journal of IFAC)
Paper: A theoretical analysis of recursive identification methods
Automatica (Journal of IFAC)
IIR system identification using cat swarm optimization
Expert Systems with Applications: An International Journal
Brief paper: Uniqueness of prediction error estimates of multivariable moving average models
Automatica (Journal of IFAC)
Input design as a tool to improve the convergence of PEM
Automatica (Journal of IFAC)
Hi-index | 22.15 |
The output error identification method is studied in various respects. The stationary points of the associated loss function are investigated. Sufficient conditions for a unique local minimum are given. The loss functions can be minimized using a quasilinearization algorithm. Such an algorithm will give good local convergence. It is, however, shown that global convergence does not always occur. The output error method is also compared with some other estimation methods from the accuracy point of view. It is proved that a prediction error method will give better accuracy. An instrumental variable technique may give better or worse accuracy depending on the actual noise correlation.