Errors-in-Variables Methods in System Identification

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Focusing on errors-in-variables (EIV) methods, this book provides a comprehensive overview for system identification, emphasizing the importance of understanding physical laws over mere predictive modeling. It addresses the complex issue of model parameter identifiability, offering sufficient conditions for it. The author explores various modeling aspects, including noise characterization and extensions to multivariable and continuous-time systems, ultimately presenting methods that effectively handle noisy data, unlike traditional least squares approaches.