论文信息 - Frequency domain maximum likelihood identification of noisy input–output models

Frequency domain maximum likelihood identification of noisy input–output models

Abstract This paper deals with the identification of errors–in–variables (EIV) models corrupted by additive and uncorrelated white Gaussian noises when the noise–free input is an arbitrary signal, not necessarily periodic. In particular, a frequency domain maximum likelihood (ML) estimator is proposed. As some other EIV estimators, this method assumes that the ratio of the noise variances is known.

Torsten Söderström | Umberto Soverini | T. Söderström | U. Soverini

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