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Unifying the landmark developments in optimal bounding ellipsoid identification
Author(s) -
Deller J. R.,
Nayeri M.,
Liu M. S.
Publication year - 1994
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.4480080105
Subject(s) - interpretability , bounding overwatch , weighting , estimator , mathematical optimization , context (archaeology) , mathematics , convergence (economics) , algorithm , computer science , identification (biology) , artificial intelligence , statistics , medicine , paleontology , biology , economics , radiology , economic growth , botany
A general class of optimal bounding elipsoid (OBE) algorithms, including all methods published to date, is unified into a single framework called the unified OBE (UOBE) algorithm. UOBE is based on generalized weighted recursive least squares in which very broad classes of ‘forgetting factors’ and data weights may be employed. Different instances of UOBE are distinguished by their weighting policies and the criteria for determining optimal weight values. A study of existing OBE algorithms, with a particular interest in the trade‐off between algorithm performance interpretability and convergence properties, is presented. Results suggest that an interpretable, converging UOBE algorithm will be found. In this context a new UOBE technique, the set membership stochastic approximation (SM‐SA) algorithm, is introduced. SM‐SA possesses interpretable optimization measures and known conditions under which its point estimator converges.