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Transfer function estimation from noisy input and output data
Author(s) -
Zheng Wei Xing
Publication year - 1998
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(199806)12:4<365::aid-acs496>3.0.co;2-a
Subject(s) - parametrization (atmospheric modeling) , transfer function , noise (video) , system identification , dimension (graph theory) , identification (biology) , noisy data , computer science , algorithm , transformation (genetics) , input/output , function (biology) , estimation theory , least squares function approximation , control theory (sociology) , mathematics , artificial intelligence , statistics , data mining , engineering , measure (data warehouse) , evolutionary biology , electrical engineering , estimator , image (mathematics) , chemistry , operating system , biochemistry , quantum mechanics , physics , gene , radiative transfer , biology , control (management) , botany , pure mathematics
Two new types of bias‐eliminated least‐squares (BELS) based algorithms are proposed for consistent identification of linear systems with noisy input and output measurements. It is shown that estimation of the noise variances can be implemented through one‐dimension over‐parametrization of the system transfer function. The two modified BELS algorithms are attractive and meaningful in that noisy data are used directly in identification with no prefiltering and a direct estimate of system parameters is given without any parameter transformation. Simulation examples are included to demonstrate the effectiveness of the two proposed algorithms. © 1998 John Wiley & Sons, Ltd.