Pintelon R., Schoukens J. System Identification: A Frequency Domain Approach

Wiley, 2012. – 788 p. – 2nd ed. – ISBN: 0470640375, 9780470640371.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 identification process, points out the possible pitfalls to the reader, and illustrates the powerful tools that are available.Readers of this Second Editon will benefit from:
State-of-the-art system identification methods for both time and frequency domain data.
New chapters on non-parametric and parametric transfer function modeling using (non-)period excitations.
Numerous examples and figures that facilitate the learning process.
A simple writing style that allows the reader to learn more about the theo?retical aspects of the proofs and algorithms.
Unlike other books in this field, System Identification, Second Edition is ideal for practicing engineers, scientists, researchers, and both master's and Ph.D. students in electrical, mechanical, civil, and chemical engineering.List of Operators and Notational Conventions.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 Know 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.