Front Cover; Dedication; Contents; Foreword; Preface; List of Figures; List of Tables; Part I: Introduction to Identification and Models for Linear Deterministic Systems; 1. Introduction; 2. A Journey into Identification; 3. Mathematical Descriptions of Processes: Models; 4. Models for Discrete-Time LTI Systems; 5. Transform-Domain Models for Linear TIme-Invariant Systems; 6. Sampling and Discretization; Part II: Models for Random Processes; 7. Random Processes; 8. Time-Domain Analysis: Correlation Functions; 9. Models for Linear Stationary Processes. 10. Fourier Transforms and Spectral Analysis of Deterministic Signals11. Spectral Representations of Random Processes; Part III: Estimation Methods; 12. Introduction to Estimation; 13. Goodness of Estimators; 14. Estimation Methods: Part I; 15. Estimation Methods: Part II; 16. Estimation of Signal Properties; Part IV: Identification of Dynamic Models
Concepts and Principles; 17. Non-Parametric and Parametric Models for Identification; 18. Predictions; 19. Identification of Parametric Time-Series Models; 20. Identification of Non-Parametric Input-Output Models. 21. Identification of Parametric Input-Output Models22. Statistical and Practical Elements of Model Building; 23. Identification of State-Space Models; 24. Case Studies; Part V: Advanced Concepts; 25. Advanced Topics in SISO Identification; 26. Linear Multivariable Identification; References; Color Insert

Master Techniques and Successfully Build Models Using a Single ResourceVital 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 discret