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.