System Identification: A Frequency Domain Approach
暫譯: 系統辨識：頻域方法

Description:

Electrical Engineering System Identification A Frequency Domain Approach 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.

 

Table of Contents:

Preface.

Acknowledgments.

List of Operators and National Conventions.

List of Symbols.

List of Abbreviations.

An Introduction to Identification.

Measurements of Frequency Response Functions.

Frequency Response Function Measurements in the Presence of Nonlinear Distortions.

Design of Excitation Signals.

Models of Linear Time-Invariant Systems.

An Intuitive Introduction to Frequency Domain Identification.

Estimation with Known Noise Model.

Estimation with Unknown Noise Model.

Model Selection and Validation.

Base Choices in System Identification.

Guidelines for the User.

Applications.

Some Linear Algebra Fundamentals.

Some Probability and Stochastic Convergence Fundamentals.

Properties of Least Squares Estimators with Deterministic Weighting.

Properties of Least Squares Estimators with Stochastic Weighting.

Identification of Semilinear Models.

Identification of Invariant of (Over) Parameterized Models.

References.

Subject Index.

Reference Index.

About the Authors.