Modelling and Identification with Rational Orthogonal Basis Functions
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Beschreibung
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.
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:
• Construction and analysis of generalized orthogonal basis function model structure;
• System Identification in a time domain setting and related issues of variance, numerics, and uncertainty bounding;
• System identification in the frequency domain;
• Design issues and optimal basis selection;
• Transformation and realization theory.
Modelling and Identification with Rational Orthogonal Basis Functions 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.
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.
Modelling and Identification with Rational Orthogonal Basis Functions 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.
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
Autor*in
Peter S.C. Heuberger
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communication control control theory design development identification information model modeling quality science signal processing system system identification uncertainty
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From the reviews:
"The book deals with the construction and use of rational orthogonal basis functions in modelling and identification of linear dynamical systems. It is written by nine authors as a research monograph and represents a survey of the field. The framework and tools are given that make it easy to evaluate how much one gains by using orthogonal basis function models … ." (Ülle Kotta, Mathematical Reviews, Issue 2006 k)
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