System Identification: A Frequency Domain Approach, Second Edition

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: * MATLAB software support for identifying multivariable systems that is freely available at the website http://booksupport.wiley.com * 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 PhD students in electrical, mechanical, civil, and chemical engineering. Content: Chapter 1 An Introduction to Identification (pages 1–31): Chapter 2 Measurement of Frequency Response Functions – Standard Solutions (pages 33–72): Chapter 3 Frequency Response Function Measurements in the Presence of Nonlinear Distortions (pages 73–118): Chapter 4 Detection, Quantification, and Qualification of Nonlinear Distortions in FRF Measurements (pages 119–150): Chapter 5 Design of Excitation Signals (pages 151–175): Chapter 6 Models of Linear Time?Invariant Systems (pages 177–224): Chapter 7 Measurement of Frequency Response Functions – The Local Polynomial Approach (pages 225–278): Chapter 8 An Intuitive Introduction to Frequency Domain Identification (pages 279–284): Chapter 9 Estimation with Known Noise Model (pages 285–382): Chapter 10 Estimation with Unknown Noise Model – Standard Solutions (pages 383–429): Chapter 11 Model Selection and Validation (pages 431–461): Chapter 12 Estimation with Unknown Noise Model – The Local Polynomial Approach (pages 463–496): Chapter 13 Basic Choices in System Identification (pages 497–530): Chapter 14 Guidelines for the User (pages 531–544): Chapter 15 Some Linear Algebra Fundamentals (pages 545–566): Chapter 16 Some Probability and Stochastic Convergence Fundamentals (pages 567–625): Chapter 17 Properties of Least Squares Estimators with Deterministic Weighting (pages 627–650): Chapter 18 Properties of Least Squares Estimators with Stochastic Weighting (pages 651–663): Chapter 19 Identification of Semilinear Models (pages 665–698): Chapter 20 Identification of Invariants of (Over)Parameterized Models (pages 699–710): How does one model a linear dynamic system from noisy data? This book presents a general Approach to this problem, with both practical examples and theoretical discussions that give the reader a sound understanding of the subject and of the pitfalls that might occur on the road from raw data to validated model. The emphasis is on robust methods that can be used with a minimum of user interaction.

Readers in many fields of engineering will gain knowledge about:

• Choice of experimental setup and experiment design
• Automatic characterization of disturbing noise
• Generation of a good plant model
• Detection, qualification, and quantification of nonlinear distortions
• Identification of continuous- and discrete-time models
• Improved model validation tools

and from the theoretical side about:

• System identification
• Interrelations between time- and frequency-domain approaches
• Stochastic properties of the estimators
• Stochastic analysis

System Identification: A Frequency Domain Approach is written for practicing engineers and scientists who do not want to delve into mathematical details of proofs. Also, it is written for researchers who wish to learn more about the theoretical aspects of the proofs. Several of the introductory chapters are suitable for undergraduates. Each chapter begins with an abstract and ends with exercises, and examples are given throughout.

...a general Approach to this problem, with both practical examples and theoretical discussions that give the reader a sound understanding of the subject and of the pitfalls that might occur on the road from raw data to validated model...

Electrical Engineering System Identification A Frequency Domain Approach How does one model a linear dynamic system from noisy data? This book presents a general approach to this problem, with both practical examples and theoretical discussions that give the reader a sound understanding of the subject and of the pitfalls that might occur on the road from raw data to validated model. The emphasis is on robust methods that can be used with a minimum of user interaction. Readers in many fields of engineering will gain knowledge about: * Choice of experimental setup and experiment design * Automatic characterization of disturbing noise * Generation of a good plant model * Detection, qualification, and quantification of nonlinear distortions * Identification of continuous- and discrete-time models * Improved model validation tools and from the theoretical side about: * System identification * Interrelations between time- and frequency-domain approaches * Stochastic properties of the estimators * Stochastic analysis System Identification: A Frequency Domain Approach is written for practicing engineers and scientists who do not want to delve into mathematical details of proofs. Also, it is written for researchers who wish to learn more about the theoretical aspects of the proofs. Several of the introductory chapters are suitable for undergraduates. Each chapter begins with an abstract and ends with exercises, and examples are given throughout