Identification of low rank vector processes
Copyright policy )Identification of low rank vector processes
We study modeling and identification of stationary processes with a spectral density matrix of low rank. Equivalently, we consider processes having an innovation of reduced dimension for which Prediction Error Methods (PEM) algorithms are not directly applicable. We show that these processes admit a special feedback structure with a deterministic feedback channel which can be used to split the identification in two steps, one of which can be based on standard algorithms while the other is based on a deterministic least squares fit. Identifiability of the feedback system is analyzed and a unique identifiable structure is characterized. Simulations show that the proposed procedure works well in some simple examples.
arXiv admin note: text overlap with arXiv:2012.05004
- Shanghai Jiao Tong University China (People's Republic of)
- Royal Institute of Technology Sweden
- University of Padua Italy
rank-reduced output noise, multivariable system identification, FOS: Electrical engineering, electronic engineering, information engineering, Systems and Control (eess.SY), Feedback control, System identification, Electrical Engineering and Systems Science - Systems and Control, low-rank process identification, feedback representation
rank-reduced output noise, multivariable system identification, FOS: Electrical engineering, electronic engineering, information engineering, Systems and Control (eess.SY), Feedback control, System identification, Electrical Engineering and Systems Science - Systems and Control, low-rank process identification, feedback representation
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).5 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!