System Identification: An Introduction (Advanced Textbooks in Control and Signal Processing)
System Identification shows the student reader how to approach the system identification problem in a systematic fashion. The process is divided into three basic steps: experimental design and data collection; model structure selection and parameter estimation; and model validation, each of which is the subject of one or more parts of the text.
Following an introduction on system theory, particularly in relation to model representation and model properties, the book contains four parts covering:
• data-based identification – non-parametric methods for use when prior system knowledge is very limited;
• time-invariant identification for systems with constant parameters;
• time-varying systems identification, primarily with recursive estimation techniques; and
• model validation methods.
A fifth part, composed of appendices, covers the various aspects of the underlying mathematics needed to begin using the text.
The book uses essentially semi-physical or gray-box modeling methods although data-based, transfer-function system descriptions are also introduced. The approach is problem-based rather than rigorously mathematical. The use of finite input–output data is demonstrated for frequency- and time-domain identification in static, dynamic, linear, nonlinear, time-invariant and time-varying systems. Simple examples are used to show readers how to perform and emulate the identification steps involved in various control design methods with more complex illustrations derived from real physical, chemical and biological applications being used to demonstrate the practical applicability of the methods described. End-of-chapter exercises (for which a downloadable instructors’ Solutions Manual is available from fill in URL here) will both help students to assimilate what they have learned and make the book suitable for self-tuition by practitioners looking to brush up on modern techniques.
Graduate and final-year undergraduate students will find this text to be a practical and realistic course in system identification that can be used for assessing the processes of a variety of engineering disciplines. System Identification will help academic instructors teaching control-related to give their students a good understanding of identification methods that can be used in the real world without the encumbrance of undue mathematical detail.
Why Read This Book
You should read this book if you want a practical, systematic route into system identification that balances theory and engineering practice. It guides you from experiment design and nonparametric estimates through parametric modeling and recursive algorithms, with an emphasis on validation and real-data issues so you can build reliable models for DSP, control, or communications tasks.
Who Will Benefit
Graduate students and practicing engineers in DSP, controls, communications, radar or audio who need to build, estimate, and validate linear system models from measured data.
Level: Intermediate — Prerequisites: Basic signals & systems, linear algebra, probability/statistics and some familiarity with numerical methods; familiarity with MATLAB for following examples is helpful.
Key Takeaways
- Design experiments and collect signals that yield informative identification data under practical constraints.
- Estimate nonparametric impulse- and frequency-response estimates using correlation and spectral methods.
- Select model structures and orders and fit parametric models (ARX, ARMAX, OE, Box-Jenkins, state-space) using least squares and prediction-error approaches.
- Implement recursive and time-varying identification algorithms (recursive LS, Kalman-filter formulations) for online/adaptive estimation.
- Validate models through residual analysis, cross-validation and frequency-domain checks to detect bias, unmodelled dynamics, and noise issues.
- Apply frequency-domain and subspace ideas to obtain reliable models in the presence of colored noise and multivariable interactions.
Topics Covered
- Introduction and problem formulation
- Basic system theory and model representations
- Experimental design and data collection
- Nonparametric data-based identification (correlation & spectral methods)
- Time‑invariant parametric identification (ARX, ARMAX, OE, Box‑Jenkins)
- Model structure selection and model order determination
- Parameter estimation methods (LS, ML/PEM) and statistical properties
- Model validation and residual analysis
- Frequency‑domain identification techniques
- State‑space models and subspace identification (overview)
- Time‑varying systems and recursive estimation (recursive LS, Kalman)
- Practical issues, implementation notes and MATLAB examples
- Appendices (numerical methods, probability, matrices)
Languages, Platforms & Tools
How It Compares
More applied and concise than Ljung's exhaustive reference 'System Identification: Theory for the User', and broader in scope than Pintelon & Schoukens' frequency‑domain–focused text which emphasizes spectral methods.
