Titlebook: Errors-in-Variables Methods in System Identification; Torsten S?derstr?m Book 2018 Springer International Publishing AG, part of Springer

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Paul E. Teschan,Allen I. Arieffmental setup, on the measurement noise, on the noise-free input signal are discussed. It is also shown what can happen when methods based on such assumptions are applied, but the assumptions fail to hold.

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M. Brock,C. Fieschi,K. Schürmannally achieved, unless more than very general assumptions are applied. Examples where introduction of additional assumptions lead to identifiability are provided. Specific identifiability results are described for the case of parametric modeling. There are also identifiability results for the special

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I. Prohovnik,E. Knudsen,J. Risberg. Further, models may be treated in the time domain or in the frequency domain, and their relations are given. Similarities and differences when treating single-input, single-output models vs. multivariable models are also discussed, as are different ways to include the noise properties in the model

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https://doi.org/10.1007/978-1-4614-4148-9dify it so as to avoid the non-consistency of the parameter estimates. One class of methods for doing so are the bias-eliminating methods. The Frisch scheme constitutes the technique for another class. Both these classes are shown to be special cases of the so-called generalized instrumental variabl