Details
Modelling and Identification with Rational Orthogonal Basis Functions
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149,79 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 06.12.2005 |
| ISBN/EAN: | 9781846281785 |
| Sprache: | englisch |
| Anzahl Seiten: | 397 |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<P>Models of dynamical systems are of great importance in almost all fields of science and engineering and specifically in control, signal processing and information science. A model is always only an approximation of a real phenomenon so that having an approximation theory which allows for the analysis of model quality is a substantial concern. The use of rational orthogonal basis functions to represent dynamical systems and stochastic signals can provide such a theory and underpin advanced analysis and efficient modelling. It also has the potential to extend beyond these areas to deal with many problems in circuit theory, telecommunications, systems, control theory and signal processing.</P>
<P><STRONG>Modelling and Identification with Rational Orthogonal Basis Functions</STRONG> affords a self-contained description of the development of the field over the last 15 years, furnishing researchers and practising engineers working with dynamical systems and stochastic processes with a standard reference work.</P>
<P><STRONG>Modelling and Identification with Rational Orthogonal Basis Functions</STRONG> affords a self-contained description of the development of the field over the last 15 years, furnishing researchers and practising engineers working with dynamical systems and stochastic processes with a standard reference work.</P>
Construction and Analysis.- Transformation Analysis.- System Identification with Generalized Orthonormal Basis Functions.- Variance Error, Reproducing Kernels, and Orthonormal Bases.- Numerical Conditioning.- Model Uncertainty Bounding.- Frequency-domain Identification in ?2.- Frequency-domain Identification in ??.- Design Issues.- Pole Selection in GOBF Models.- Transformation Theory.- Realization Theory.
<P>Peter Heuberger obtained his M.Sc. degree in Mathematics at Groningen University and his Ph.D. from Delft University of Technology (both in the Netherlands). As well as his part-time position at the Delft Center for Systems and Control, he is a researcher at the Netherlands Environmental Assessment Agency in the Department of Information Services and Methodology.</P>
<P> </P>
<P>Paul Van den Hof has been Professor of Signals, Systems and Control at the Delft University of Technology since 1999 and th co-Director of the Center for Systems and Control since 2003.</P>
<P>Professor Van den Hof's research interests include system identification, parametrization, signal processing and robust control design, physical measurement systems and industrial process control. He is a member of the IFAC Council (serving between 1999 and 2005), an elected member of the Board of Governors of the IEEE Control Systems Society between 2003 and 2005 and <EM>Automatica</EM> Editor for Rapid Publications.</P>
<P> </P>
<P>Professor Bo Wahlberg has held the Chair of Automatic Control at the Royal Institute of Technology (KTH), Stockholm since 1991. He has served as visiting professor at Stanford University and was a vice-president of KTH between 1999 and 2001.</P>
<P>Professor Wahlberg's extensive c.v. can be found at http://www.s3.kth.se/~bo/cv.pdf</P>
<P> </P>
<P>Paul Van den Hof has been Professor of Signals, Systems and Control at the Delft University of Technology since 1999 and th co-Director of the Center for Systems and Control since 2003.</P>
<P>Professor Van den Hof's research interests include system identification, parametrization, signal processing and robust control design, physical measurement systems and industrial process control. He is a member of the IFAC Council (serving between 1999 and 2005), an elected member of the Board of Governors of the IEEE Control Systems Society between 2003 and 2005 and <EM>Automatica</EM> Editor for Rapid Publications.</P>
<P> </P>
<P>Professor Bo Wahlberg has held the Chair of Automatic Control at the Royal Institute of Technology (KTH), Stockholm since 1991. He has served as visiting professor at Stanford University and was a vice-president of KTH between 1999 and 2001.</P>
<P>Professor Wahlberg's extensive c.v. can be found at http://www.s3.kth.se/~bo/cv.pdf</P>
<P>Models of dynamical systems are of great importance in almost all fields of science and engineering and specifically in control, signal processing and information science. A model is always only an approximation of a real phenomenon so that having an approximation theory which allows for the analysis of model quality is a substantial concern. The use of rational orthogonal basis functions to represent dynamical systems and stochastic signals can provide such a theory and underpin advanced analysis and efficient modelling. It also has the potential to extend beyond these areas to deal with many problems in circuit theory, telecommunications, systems, control theory and signal processing.</P>
<P>Nine international experts have contributed to this work to produce thirteen chapters that can be read independently or as a comprehensive whole with a logical line of reasoning:</P>
<P> </P>
<P>• Construction and analysis of generalized orthogonal basis function model structure; </P>
<P></P>
<P>• System Identification in a time domain setting and related issues of variance, numerics, and uncertainty bounding;
<P></P>
<P>• System identification in the frequency domain;
<P></P>
<P>• Design issues and optimal basis selection;
<P></P>
<P>• Transformation and realization theory.
<P>
<P><EM>Modelling and Identification with Rational Orthogonal Basis Functions</EM> affords a self-contained description of the development of the field over the last 15 years, furnishing researchers and practising engineers working with dynamical systems and stochastic processes with a standard reference work.</P>
<P>Nine international experts have contributed to this work to produce thirteen chapters that can be read independently or as a comprehensive whole with a logical line of reasoning:</P>
<P> </P>
<P>• Construction and analysis of generalized orthogonal basis function model structure; </P>
<P></P>
<P>• System Identification in a time domain setting and related issues of variance, numerics, and uncertainty bounding;
<P></P>
<P>• System identification in the frequency domain;
<P></P>
<P>• Design issues and optimal basis selection;
<P></P>
<P>• Transformation and realization theory.
<P>
<P><EM>Modelling and Identification with Rational Orthogonal Basis Functions</EM> affords a self-contained description of the development of the field over the last 15 years, furnishing researchers and practising engineers working with dynamical systems and stochastic processes with a standard reference work.</P>
Will teach the reader how to use an important technique in modelling and system identification with a wide range of engineering applications Includes supplementary material: sn.pub/extras
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