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  1. Home
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  3. Nonparametric System Identification
  • Cited by 117
Publisher:
Cambridge University Press
Online publication date:
November 2009
Print publication year:
2008
Online ISBN:
9780511536687
DOI:
https://doi.org/10.1017/CBO9780511536687
Subjects:
Communications and Signal Processing, Engineering, Control Systems and Optimisation
Information

Book description

Presenting a thorough overview of the theoretical foundations of non-parametric system identification for nonlinear block-oriented systems, this book shows that non-parametric regression can be successfully applied to system identification, and it highlights the achievements in doing so. With emphasis on Hammerstein, Wiener systems, and their multidimensional extensions, the authors show how to identify nonlinear subsystems and their characteristics when limited information exists. Algorithms using trigonometric, Legendre, Laguerre, and Hermite series are investigated, and the kernel algorithm, its semirecursive versions, and fully recursive modifications are covered. The theories of modern non-parametric regression, approximation, and orthogonal expansions, along with new approaches to system identification (including semiparametric identification), are provided. Detailed information about all tools used is provided in the appendices. This book is for researchers and practitioners in systems theory, signal processing, and communications and will appeal to researchers in fields like mechanics, economics, and biology, where experimental data are used to obtain models of systems.

Reviews

Review of the hardback:'All chapters end with precise technical derivations of the presented material and bibliographical notes providing numerous references to the related literature … The monograph fills the gap in the system identification monographic literature dealing mainly with the parametric approach, and can be recommended for researchers and practitioners interested in system identification problems where a priori information is very limited and only experimental data can be reliably used to recover system models.'

Source: Zentralblatt MATH

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