Title :
4SID linear regression
Author :
Wahlberg, Bo ; Jansson, Magnus
Author_Institution :
R. Inst. of Technol., Stockholm, Sweden
Abstract :
State-space subspace system identification (4SID) has been suggested as an alternative to more traditional prediction error system identification, such as ARX least squares estimation. The aim of this note is to analyse the connections between these two different approaches to system identification. The conclusion is that 4SID can be viewed as a linear regression multistep ahead prediction error method, with certain rank constraints. This allows us to analyse 4SID methods within the standard framework of system identification and linear regression estimation. For example, it is shown that ARX models have nice properties in terms of 4SID identification. From a linear regression model, estimates of the extended observability matrix are found. Results from an asymptotic analysis are presented, i.e. explicit formulas for the asymptotic variances of the pole estimation error are given. From these expressions, some difficulties in choosing user specified parameters are pointed out in an example
Keywords :
identification; matrix algebra; observability; poles and zeros; state-space methods; statistical analysis; 4SID linear regression; asymptotic variances; extended observability matrix; linear regression estimation; linear regression multistep-ahead prediction error method; pole estimation error; prediction error system identification; rank constraints; state-space subspace system identification; user-specified parameters; Additive noise; Automatic control; Iterative algorithms; Linear regression; Noise measurement; Observability; Signal processing; Space technology; State-space methods; System identification;
Conference_Titel :
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location :
Lake Buena Vista, FL
Print_ISBN :
0-7803-1968-0
DOI :
10.1109/CDC.1994.411364