System Identification: A Frequency Domain Approach
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.
Why Read This Book
You will learn practical, experimentally oriented frequency-domain methods to build reliable dynamical models from measured data, with clear guidance on pitfalls and validation. The book emphasizes hands‑on use of FFT/spectral tools and MATLAB code so you can move from raw measurements to validated transfer-function and noise models for control, communications, audio, and radar applications.
Who Will Benefit
Practicing engineers and graduate students with a background in signals and systems who need robust frequency-domain identification techniques for control, communications, audio/speech, or radar signal processing.
Level: Intermediate — Prerequisites: Undergraduate-level signals and systems (linear systems, convolution, Fourier/ Laplace transforms), basic probability/statistics, linear algebra, and familiarity with MATLAB.
Key Takeaways
- Apply nonparametric and parametric frequency-domain identification methods to estimate frequency response functions and transfer functions from measurement data.
- Design experiments and use FFT-based spectral analysis to obtain reliable spectral estimates and reduce bias from noise and leakage.
- Assess model quality using statistical tools and error analysis, and compare frequency-domain and time-domain approaches.
- Handle noise, disturbance models, and closed-loop identification issues typical in real experiments.
- Use the provided MATLAB scripts and workflows to implement identification procedures and validate models for practical systems.
Topics Covered
- Introduction to system identification and motivations
- Frequency-domain fundamentals: Fourier transforms and spectra
- Nonparametric identification: spectral estimation and FRF
- Parametric frequency-domain estimation: model structures and fitting
- Estimation accuracy: bias, variance, and confidence intervals
- Noise and disturbance modeling, closed‑loop identification
- Experiment design and input signal selection
- Multivariable systems and cross-spectral methods
- Comparison with time-domain methods and combined strategies
- Practical implementation: MATLAB examples and algorithms
- Advanced topics: adaptive/recursive frequency-domain methods and model validation
Languages, Platforms & Tools
How It Compares
Compared with Ljung's authoritative System Identification (which emphasizes time-domain theory and broad coverage), Pintelon's book focuses tightly on frequency-domain methods and hands-on experimental practice with MATLAB.
