- Title
- Maximum-likelihood parameter estimation of bilinear systems
- Creator
- Gibson, S.; Wills, Adrian G.; Ninness, Brett
- Relation
- IEEE Transactions on Automatic Control Vol. 50, no. 10, p. 1581-1596
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2005
- Description
- This paper addresses the problem of estimating the parameters in a multivariable bilinear model on the basis of observed input-output data. The main contribution is to develop, analyze, and empirically study new techniques for computing a maximum-likelihood based solution. In particular, the emphasis here is on developing practical methods that are illustrated to be numerically reliable, robust to choice of initialization point, and numerically efficient in terms of how computation and memory requirements scale relative to problem size. This results in new methods that can be reliably deployed on systems of nontrivial state, input and output dimension. Underlying these developments is a new approach (in this context) of employing the expectation-maximization method as a means for robust and gradient free computation of the maximum-likelihood solution.
- Subject
- bilinear systems; maximum likelihood (ML); parameter estimation; system; identification; em algorithm; subspace identification; linear-systems; missing data; maximization; subject
- Identifier
- http://hdl.handle.net/1959.13/25076
- Identifier
- uon:240
- Identifier
- ISSN:0018-9286
- Rights
- Copyright © 2005 IEEE. Reprinted from IEEE Transactions on Automatic Control. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Newcastle's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Language
- eng
- Full Text
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