Linked Life Data

1487284

Source:http://linkedlifedata.com/resource/pubmed/id/1487284

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
12
pubmed:dateCreated
1993-2-25
pubmed:abstractText
The theory underlying a new method for the identification of time-varying systems is described. The method uses singular value decomposition to obtain least-squares estimates of time-varying impulse response functions from an ensemble of input-output realizations. No a priori assumptions regarding the system structure or form of the time-variation are required and there are few restrictions on the input signal. Simulation studies, using a model of time-varying joint dynamics, show that the method can track rapid changes in system dynamics accurately and is robust in the presence of output noise. An application of the method is demonstrated by using it to track dynamic ankle stiffness during a rapid, voluntary, isometric contraction. During the transient phase of the contraction, low-frequency ankle stiffness gain decreased in a manner which could not be described with the second-order model of joint dynamics often used under stationary conditions.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0018-9294
pubmed:author
pubmed:issnType
Print
pubmed:volume
39
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1213-25
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
1992
pubmed:articleTitle
Identification of time-varying biological systems from ensemble data.
pubmed:affiliation
Department of Biomedical Engineering, McGill University, Montréal, P.Q., Canada.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't