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Detecting parameter identifiability problems in system identification
Author(s) -
Niu Steve S.,
Fisher D. Grant
Publication year - 1997
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/(sici)1099-1115(199711)11:7<603::aid-acs455>3.0.co;2-h
Subject(s) - identifiability , identification (biology) , computation , simple (philosophy) , system identification , least squares function approximation , computer science , estimation theory , parameter identification problem , mathematics , algorithm , mathematical optimization , control theory (sociology) , artificial intelligence , model parameter , data mining , machine learning , statistics , philosophy , botany , epistemology , estimator , biology , measure (data warehouse) , control (management)
A simple, practical and unified method is presented for detecting parameter identifiability problems caused by non‐persistent excitation, overparametrization and/or output feedback within the system to be identified. All the required information is generated inherently by the multiple‐model least‐squares (MMLS) method and/or the augmented UD identification (AUDI) algorithm developed by the authors, so very little extra computation is required. Several examples are included to illustrate the principles involved and their application. © 1997 John Wiley & Sons, Ltd.