Abstract/Details

Semi-blind robust identification and model (in)validation


2007 2007

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Abstract (summary)

In this thesis, we study a so-called semi-blind robust identification motivated from the fact that sometimes for system Identification only partial input data is exactly known. Derived from a time-domain algorithm for robust identification, this semi-blind robust identification is stated as a non convex problem. We develop a convex relaxation, by combining two variables into a new variable, to reduce it to an LMI optimization problem. Applying this convex relaxation, a macro-economy modelling problem can be solved. For future work of application on Intrusion Detection, a sampling algorithm for blind identification is also briefly presented.

Accordingly, we consider the problem of semi-blind (in)validation which is shown to be non convex. Two different relaxations—a deterministic and a risk-adjusted convex relaxation—are explored to solve this non convex problem. We demonstrate an application of the semi-blind (in)validation on the problem of detecting and isolating faults from noisy input-output measurements. The results of this application using both two relaxations are presented through an experimental example.

Furthermore, the problem of identification of Wiener Systems, a special type of nonlinear systems, is analyzed from a set-membership standpoint. We propose an algorithm for time-domain based identification by pursuing a risk-adjusted approach to reduce it to a convex optimization problem. An arising non-trivial problem in computer vision, tracking a human in a sequence of frames, can be solved by modelling the plant as Wiener system using the proposed identification method.

Indexing (details)


Subject
Automotive engineering;
Electrical engineering;
Operations research
Classification
0540: Automotive engineering
0544: Electrical engineering
0796: Operations research
Identifier / keyword
Applied sciences; Control-oriented systems; Model validation; Robust identification; Semi-blind (in)validation
Title
Semi-blind robust identification and model (in)validation
Author
Ma, Wenjing
Number of pages
143
Publication year
2007
Degree date
2007
School code
0176
Source
DAI-B 71/02, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9781109616002
University/institution
The Pennsylvania State University
University location
United States -- Pennsylvania
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3393785
ProQuest document ID
304836825
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304836825
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