Abstract/Details

System identification: Signal causality and feedback determination


1991 1991

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Abstract (summary)

The problem of determining a bivariate innovations model of two linear stationary stochastic processes in the context of their causal relationships is considered. It is approached in two ways which lead to dual identification algorithms. One method is the direct approach which directly uses the observation data, while the other one is the indirect approach which uses the whitened versions of the observation data instead. Crosscorrelation estimators are employed to extract the causal information in the joint system. As a result of applying the two approaches, closed-form formulae are derived that clearly indicate the dependence of the coupling parameters in the bivariate innovations model on the crosscorrelations of its member signals or their univariate innovations. These formulae assume the general feedback structure and includes those special structures in which only one causal relation exists or no causal relation exists in the joint system. Simulation examples are presented to illustrate the effectiveness of the two methods. Monte Carlo experiments are also conducted to evaluate and compare the statistical properties of the estimates of the coupling parameters of several simulated bivariate systems as the two dual algorithms are applied to them.

Indexing (details)


Subject
Electrical engineering
Classification
0544: Electrical engineering
Identifier / keyword
Applied sciences
Title
System identification: Signal causality and feedback determination
Author
Qian, Cunlin
Number of pages
165
Publication year
1991
Degree date
1991
School code
0022
Source
DAI-B 52/06, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
Advisor
Stirling, Wynn C.
University/institution
Brigham Young University
University location
United States -- Utah
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
9134604
ProQuest document ID
303975526
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/303975526
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