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Identification of a class of dynamic errors‐in‐variables models
Author(s) -
Zheng WeiXing,
Feng ChunBo
Publication year - 1992
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.4480060503
Subject(s) - noise (video) , estimator , a priori and a posteriori , identification (biology) , computer science , system identification , white noise , noise measurement , class (philosophy) , least squares function approximation , algorithm , mathematics , control theory (sociology) , statistics , data modeling , artificial intelligence , noise reduction , philosophy , botany , epistemology , database , image (mathematics) , biology , control (management)
Dynamic errors‐in‐variables (EV) models are a new type of linear system models and have found extensive practical applications. One common and important concern with EV models is how to remove noise‐induced bias in parameter estimators. In this paper some significant extensions to the newly established bias‐eliminated least‐squares (BELS) method are made, so that this BELS method can be applied to unbiased identification of a general class of dynamic EV models where input noise is white noise and output noise is correlated noise but the noise statistics are unknown a priori . Though still based on the bias correction principle, this method is very meaningful in that it presents a novel and efficient way of utilizing signal‐processing techniques to draw much more useful information from sampled data in order to get desirable identification results. The performance of the proposed method is illustrated by numerical examples.