Title :
System identification using partitioned least squares
Author :
Karny, M. ; Warwick, K.
Author_Institution :
Inst. of Inf. Theory & Autom., Czech Acad. of Sci., Prague, Czech Republic
fDate :
5/1/1995 12:00:00 AM
Abstract :
A novel partitioned least squares (PLS) algorithm is presented, in which estimates from several simple system models are combined by means of a Bayesian methodology of pooling partial knowledge. The method has the added advantage that, when the simple models are of a similar structure, it lends itself directly to parallel processing procedures, thereby speeding up the entire parameter estimation process by several factors
Keywords :
Bayes methods; autoregressive processes; least squares approximations; parameter estimation; Bayesian method; parallel processing; parameter estimation; partitioned least squares; system identification; system models;
Journal_Title :
Control Theory and Applications, IEE Proceedings -
DOI :
10.1049/ip-cta:19951877
