Principles of system identification : theory and practice /

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 Syste...

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Bibliographic Details
Main Author:	Tangirala, Arun K., 1974- (Author)
Corporate Author:	Taylor & Francis
Format:	eBook
Language:	English
Published:	Boca Raton, FL : CRC Press, [2015]
Subjects:	System identification. Linear systems. SCIENCE > System Theory. TECHNOLOGY & ENGINEERING > Operations Research. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.