E.V. Kozlov and O.A. Katsyuba (Russia)
Parametric identification, multilinked dynamical system
It is well-known that the least-squares identification method generally gives biased parameter estimates when the observed input-output data are corrupted with noise. This paper shows a new interpretation of the subspace-based identification methods by using least-squares methods. The estimations of unknown true meanings are defined from condition of the minimum of the amount weighted square-law deflections of the generalized mistake. It has been proven the consistency of the obtained values. This method of identification does not require the knowledge of the noise and signal propagation.
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