Principles of System Identification: Theory and Practice

Master Techniques and Successfully Build Models Using a Single Resource

Vital to all data-driven or measurement-based process operations, system identification is an interface that is based on observational science, and centers on developing mathematical models from observed data. Principles of System Identification: Theory and Practice is an introductory-level book that presents the basic foundations and underlying methods relevant to system identification. The overall scope of the book focuses on system identification with an emphasis on practice, and concentrates most specifically on discrete-time linear system identification.

Useful for Both Theory and Practice

The book presents the foundational pillars of identification, namely, the theory of discrete-time LTI systems, the basics of signal processing, the theory of random processes, and estimation theory. It explains the core theoretical concepts of building (linear) dynamic models from experimental data, as well as the experimental and practical aspects of identification. The author offers glimpses of modern developments in this area, and provides numerical and simulation-based examples, case studies, end-of-chapter problems, and other ample references to code for illustration and training.

Comprising 26 chapters, and ideal for coursework and self-study, this extensive text:

• Provides the essential concepts of identification
• Lays down the foundations of mathematical descriptions of systems, random processes, and estimation in the context of identification
• Discusses the theory pertaining to non-parametric and parametric models for deterministic-plus-stochastic LTI systems in detail
• Demonstrates the concepts and methods of identification on different case-studies
• Presents a gradual development of state-space identification and grey-box modeling
• Offers an overview of advanced topics of identification namely the linear time-varying (LTV), non-linear, and closed-loop identification
• Discusses a multivariable approach to identification using the iterative principal component analysis
• Embeds MATLAB^® codes for illustrated examples in the text at the respective points

Principles of System Identification: Theory and Practice presents a formal base in LTI deterministic and stochastic systems modeling and estimation theory; it is a one-stop reference for introductory to moderately advanced courses on system identification, as well as introductory courses on stochastic signal processing or time-series analysis.

Table of Contents

Part I: Introduction to Identification and Models for Linear Deterministic Systems
Chapter 1. Introduction
Chapter 2. A Journey into Identification
Chapter 3. Mathematical Descriptions of Processes: Models
Chapter 4. Models for Discrete-Time LTI Systems
Chapter 5. Transform-Domain Models for Linear TIme-Invariant Systems
Chapter 6. Sampling and Discretization

Part II: Models for Random Processes
Chapter 7. Random Processes
Chapter 8. Time-Domain Analysis: Correlation Functions
Chapter 9. Models for Linear Stationary Processes
Chapter 10. Fourier Transforms and Spectral Analysis of Deterministic Signals
Chapter 11. Spectral Representations of Random Processes

Part III: Estimation Methods
Chapter 12. Introduction to Estimation
Chapter 13. Goodness of Estimators
Chapter 14. Estimation Methods: Part I
Chapter 15. Estimation Methods: Part II
Chapter 16. Estimation of Signal Properties

Part IV: Identification of Dynamic Models – Concepts and Principles
Chapter 17. Non-Parametric and Parametric Models for Identification
Chapter 18. Predictions
Chapter 19. Identification of Parametric Time-Series Models
Chapter 20. Identification of Non-Parametric Input-Output Models
Chapter 21. Identification of Parametric Input-Output Models
Chapter 22. Statistical and Practical Elements of Model Building
Chapter 23. Identification of State-Space Models
Chapter 24. Case Studies

Part V: Advanced Concepts
Chapter 25. Advanced Topics in SISO Identification
Chapter 26. Linear Multivariable Identification