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Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculationsThis paper discusses numerical aspects of computing maximum likelihood estimates for linear dynamical systems in state-vector form. Different gradient-based nonlinear programming methods are discussed in a unified framework and their applicability to maximum likelihood estimation is examined. The problems due to singular Hessian or singular information matrix that are common in practice are discussed in detail and methods for their solution are proposed. New results on the calculation of state sensitivity functions via reduced order models are given. Several methods for speeding convergence and reducing computation time are also discussed.
Document ID
19750031633
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Gupta, N. K.
(Systems Control, Inc. Palo Alto, Calif., United States)
Mehra, R. K.
(Harvard University Cambridge, Mass., United States)
Date Acquired
August 8, 2013
Publication Date
December 1, 1974
Publication Information
Publication: IEEE Transactions on Automatic Control
Volume: AC-19
Subject Category
Numerical Analysis
Accession Number
75A15705
Funding Number(s)
CONTRACT_GRANT: N00014-72-C-0328
CONTRACT_GRANT: NAS4-2068
Distribution Limits
Public
Copyright
Other

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