System Identification
A Frequency Domain Approach
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Beschreibung
System identification is a general term used to describe mathematical tools and algorithms that build dynamical models from measured data. Used for prediction, control, physical interpretation, and the designing of any electrical systems, they are vital in the fields of electrical, mechanical, civil, and chemical engineering.
Focusing mainly on frequency domain techniques, System Identification: A Frequency Domain Approach, Second Edition also studies in detail the similarities and differences with the classical time domain approach. It high??lights many of the important steps in the identification process, points out the possible pitfalls to the reader, and illustrates the powerful tools that are available.
Readers of this Second Editon will benefit from:
* MATLAB(r) software support for identifying multivariable systems that is freely available at the website http://booksupport.wiley.com
* State-of-the-art system identification methods for both time and frequency domain data
* New chapters on non-parametric and parametric transfer function modeling using (non-)period excitations
* Numerous examples and figures that facilitate the learning process
* A simple writing style that allows the reader to learn more about the theo??retical aspects of the proofs and algorithms
Unlike other books in this field, System Identification, Second Edition is ideal for practicing engineers, scientists, researchers, and both master's and PhD students in electrical, mechanical, civil, and chemical engineering.
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.
Autor*in
Rik Pintelon
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