System identification : a frequency domain approach /

• Κύριος συγγραφέας: Pintelon, R. (Rik)
• Συγγραφή απο Οργανισμό/Αρχή: Wiley InterScience (Online service)
• Άλλοι συγγραφείς: Schoukens, J. (Johan)
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Hoboken, N.J. : Wiley ; [2012]; Piscataway, NJ : IEEE Press, [2012]
• Έκδοση: 2nd ed.
• Θέματα: System identification.; SCIENCE > System Theory.; TECHNOLOGY & ENGINEERING > Operations Research.; Electronic books.

 System identification is a general term used to describe mathematical tools and algorithms that build dynamical models from measured data. Used for prediction, control, physical interpretation, and the designing of any electrical systems, they are vital in the fields of electrical, mechanical, civil, and chemical engineering. Focusing mainly on frequency domain techniques, System Identification: A Frequency Domain Approach, Second Edition also studies in detail the similarities and differences with the classical time domain approach. It high??lights many of the important steps in the identific. 

 An Introduction to Identification --   Measurement of Frequency Response Functions -- Standard Solutions --   Frequency Response Function Measurements in the Presence of Nonlinear Distortions --   Detection, Quantification, and Qualification of Nonlinear Distortions in FRF Measurements --   Design of Excitation Signals --   Models of Linear Time-Invariant Systems --   Measurement of Frequency Response Functions -- The Local Polynomial Approach --   An Intuitive Introduction to Frequency Domain Identification --   Estimation with Known Noise Model --   Estimation with Unknown Noise Model -- Standard Solutions --   Model Selection and Validation --   Estimation with Unknown Noise Model -- The Local Polynomial Approach --   Basic Choices in System Identification --   Guidelines for the User --   Some Linear Algebra Fundamentals --   Some Probability and Stochastic Convergence Fundamentals --   Properties of Least Squares Estimators with Deterministic Weighting --   Properties of Least Squares Estimators with Stochastic Weighting --   Identification of Semilinear Models --   Identification of Invariants of (Over)Parameterized Models.