System Identification
A Frequency Domain Approach
2. Auflage April 2012
788 Seiten, Hardcover
Wiley & Sons Ltd
This book is focused on modeling of linear dynamic systems. These models can be continuous-time or discrete-time. In both cases, the model can be written as a rational form in a generalized frequency variable. The aim of the book is to identify the parameters of these rational forms starting from noisy measurements of the input and output signals. The complete identification process is covered from starting from the experiment design, the parameter estimation-model selection, and the model validation. Since all real life systems behave to some extent nonlinearly, the influence of the nonlinearities on the linear idenfication framework is also studied in detail. The methods are illustrated on a very wide variety of practical examples coming from the electrical, mechanical, acoustical, and chemical fields. A great deal of attention will be spent on the practical applicability of the identification techniques. For clarity of exposition, the emphasis will be put on single input, single output systems. Most the presented results are also valid for multivariate systems and where necessary, the subtle differences will be explained.
Preface to the Second Edition
Acknowledgments
List of Operators and Notational Conventions
List of Symbols
List of Abbreviations
Chapter 1 An Introduction to Identification
Chapter 2 Measurement of Frequency Response Functions - Standard Solutions
Chapter 3 Frequency Response Function Measurements in the Presence of Nonlinear Distortions
Chapter 4 Detection, Quantification, and Qualification of Nonlinear Distortions in FRF Measurements
Chapter 5 Design of Excitation Signals
Chapter 6 Models of Linear Time-Invariant Systems
Chapter 7 Measurement of Frequency Response Functions - The Local Polynomial Approach
Chapter 8 An Intuitive Introduction to Frequency Domain Identification
Chapter 9 Estimation with Know Noise Model
Chapter 10 Estimation with Unknown Noise Model - Standard Solutions
Chapter 11 Model Selection and Validation
Chapter 12 Estimation with Unknown Noise Model - The Local Polynomial Approach
Chapter 13 Basic Choices in System Identification
Chapter 14 Guidelines for the User
Chapter 15 Some Linear Algebra Fundamentals
Chapter 16 Some Probability and Stochastic Convergence Fundamentals
Chapter 17 Properties of Least Squares Estimators with Deterministic Weighting
Chapter 18 Properties of Least Squares Estimators with Stochastic Weighting
Chapter 19 Identification of Semilinear Models
Chapter 20 Identification of Invariants of (Over) Parameterized Models
References
Subject Index
Author Index
About the Authors
JOHAN SCHOUKENS, PhD, serves as a full-time professor in the ELEC Department at the Vrije Universiteit Brussel. He has been a Fellow of IEEE since 1997 and was the recipient of the 2003 IEEE Instrumentation and Measurement Society Distinguished Service Award.
