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Recursive subspace identification based on instrumental variable unconstrained quadratic optimization
Author(s) -
Mercère G.,
Lecoeuche S.,
Lovera M.
Publication year - 2004
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.835
Subject(s) - subspace topology , instrumental variable , identification (biology) , quadratic equation , class (philosophy) , variable (mathematics) , mathematical optimization , computer science , algorithm , mathematics , artificial intelligence , machine learning , mathematical analysis , botany , geometry , biology
Abstract The problem of the recursive formulation of the MOESP class of subspace identification algorithms is considered and two novel instrumental variable approaches are introduced. The first one leads to an RLS‐like implementation, the second to a gradient type iteration. The relative merits of both approaches are analysed and discussed, while simulation results are used to compare their performance with one of the existing techniques. Copyright © 2004 John Wiley & Sons, Ltd.