Variance Results for Parallel Cascade Serial Systems
2014 (English)In: Proceedings of 19th IFAC World Congress, 2014Conference paper (Refereed)
Modelling dynamic networks is important in different fields of science. At present, little is known about how different inputs and sensors contribute to the statistical properties concerning an estimate of a specific dynamic system in a network. We consider two forms of parallel serial structures, one multiple-input-multiple-output structure and one single-input multiple-output structure. The quality of the estimated models is analysed by means of the asymptotic covariance matrix, with respect to input signal characteristics, noise characteristics, sensor locations and previous knowledge about the remaining systems in the network. It is shown that an additive property applies to the information matrix for the considered structures. The impact of input signal selection, sensor locations and incorporation of previous knowledge isillustrated by simple examples.
Place, publisher, year, edition, pages
IdentifiersURN: urn:nbn:se:kth:diva-159092OAI: oai:DiVA.org:kth-159092DiVA: diva2:782486
IFAC 2014, 19th World Congress of the International Federation of Automatic Control
FunderSwedish Research Council, 621-2009-4017EU, European Research Council, 267381
QC 201503062015-01-212015-01-212015-03-06Bibliographically approved