[内容简介]
How does one model a linear dynamic system from noisy data? This book presents a general Approach to this problem, with both practical examples and theoretical discussions that give the reader a sound understanding of the subject and of the pitfalls that might occur on the road from raw data to validated model. The emphasis is on robust methods that can be used with a minimum of user interaction.

Readers in many fields of engineering will gain knowledge about:

• Choice of experimental setup and experiment design
• Automatic characterization of disturbing noise
• Generation of a good plant model
• Detection, qualification, and quantification of nonlinear distortions
• Identification of continuous- and discrete-time models
• Improved model validation tools

and from the theoretical side about:

• System identification
• Interrelations between time- and frequency-domain approaches
• Stochastic properties of the estimators
• Stochastic analysis

System Identification: A Frequency Domain Approach is written for practicing engineers and scientists who do not want to delve into mathematical details of proofs. Also, it is written for researchers who wish to learn more about the theoretical aspects of the proofs. Several of the introductory chapters are suitable for undergraduates. Each chapter begins with an abstract and ends with exercises, and examples are given throughout.

...a general Approach to this problem, with both practical examples and theoretical discussions that give the reader a sound understanding of the subject and of the pitfalls that might occur on the road from raw data to validated model...

[目录]
      Preface
                  Acknowledgments
                  List of Operators and Notational Conventions
                  List of Symbols
                  List of Abbreviations
            Ch. 1       An Introduction to Identification        
         Ch. 2       Measurement of Frequency Response Functions
            Ch. 3       Frequency Response Function Measurements in the Presence of Nonlinear Distortions
            Ch. 4       Design of Excitation Signals
            Ch. 5       Models of Linear Time-Invariant Systems
            Ch. 6       An Intuitive Introduction to Frequency Domain Identification
            Ch. 7     Estimation with Known Noise Model
            Ch. 8       Estimation with Unknown Noise Model
          Ch. 9       Model Selection and Validation              
        Ch. 10    Basic Choices in System Identification
            Ch. 11    Guidelines for the User
            Ch. 12       Applications
            Ch. 13       Some Linear Algebra Fundamentals
            Ch. 14       Some Probability and Stochastic Convergence Fundamentals
            Ch. 15       Properties of Least Squares Estimators with Deterministic Weighting
            Ch. 16       Properties of Least Squares Estimators with Stochastic Weighting
            Ch. 17       Identification of Semilinear Models
            Ch. 18       Identification of Invariants of (Over)Parameterized Models
                  References
                  Subject Index
Reference Index
About the Authors