Cumulant-based identification of noisy closed loop systems

Delopoulos, A; Giannakis, GB

dc.contributor.author	Delopoulos, A	en
dc.contributor.author	Giannakis, GB	en
dc.date.accessioned	2014-03-01T01:11:50Z
dc.date.available	2014-03-01T01:11:50Z
dc.date.issued	1996	en
dc.identifier.issn	0890-6327	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/11828
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0030105027&partnerID=40&md5=ea1f36ab95101b3fddc9c93fa3fde5d0	en
dc.subject	Closed loop systems	en
dc.subject	Parameter estimation	en
dc.subject	Recursive estimation	en
dc.subject	Statistics (cumulants)	en
dc.subject	System identification	en
dc.subject.classification	Automation & Control Systems	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Algorithms	en
dc.subject.other	Approximation theory	en
dc.subject.other	Computer simulation	en
dc.subject.other	Identification (control systems)	en
dc.subject.other	Mathematical models	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Performance	en
dc.subject.other	Recursive functions	en
dc.subject.other	Signal to noise ratio	en
dc.subject.other	Spurious signal noise	en
dc.subject.other	Statistical methods	en
dc.subject.other	Additive noise	en
dc.subject.other	Cumulants	en
dc.subject.other	Linear equations	en
dc.subject.other	Mean squared error	en
dc.subject.other	Closed loop control systems	en
dc.title	Cumulant-based identification of noisy closed loop systems	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	1996	en
heal.abstract	Conventional parameter estimation approaches fail to identify linear systems operating in closed loop when both input and output measurements are contaminated by additive noise of unknown (cross-)spectral characteristics. However, even in the absence of measurement noise, parameter estimation is involved owing to the additive system noise entering the loop. The present work introduces a novel criterion which is theoretically insensitive to a class of disturbances and yields the same parameter estimates that one obtains using mean squared error (MSE) minimization in the absence of noise. A strongly convergent sample-based approximation of the proposed criterion is introduced for consistent parameter estimation in practice. It is also shown that in the common case of ARMA modelling the resulting parameter estimates coincide with those obtained from a set of linear equations which can be solved using a time-recursive algorithm. Simulation results are presented to verify the performance of the proposed schemes in low-signal-to-noise-ratio environments.	en
heal.publisher	JOHN WILEY & SONS LTD	en
heal.journalName	International Journal of Adaptive Control and Signal Processing	en
dc.identifier.isi	ISI:A1996UD00700011	en
dc.identifier.volume	10	en
dc.identifier.issue	2	en
dc.identifier.spage	303	en
dc.identifier.epage	317	en