Nonlinear System Identification by Haar Wavelets

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In order to precisely model real-life systems or man-made devices, both nonlinear and dynamic properties need to be taken into account. The generic, black-box model based on Volterra and Wiener series is capable of representing fairly complicated nonlinear and dynamic interactions, however, the resulting identification algorithms are impractical, mainly due to their computational complexity. One of the alternatives offering fast identification algorithms is the block-oriented approach, in which systems of relatively simple structures are considered. The book provides nonparametric identification algorithms designed for such systems together with the description of their asymptotic and computational properties.

Provides nonparametric algorithms based on standard and unbalanced Haar bases Demonstrates applicability of nonlinear approximation schemes to nonlinear system identification Offers fast identification routines Includes supplementary material: sn.pub/extras

Autorentext
Dr. Przemysaw liwiski is an assistant professor at the Wrocaw University of Technology, where he received his master's degree in 1996 and his PhD in 2000. For his master's degree he developed an integrated development environment with a software emulator of a micro-controller. His PhD dissertation addressed the problems of nonlinear system identification using linear wavelet estimation algorithms.

Klappentext

In order to precisely model real-life systems or man-made devices, both nonlinear and dynamic properties need to be taken into account. The generic, black-box model based on Volterra and Wiener series is capable of representing fairly complicated nonlinear and dynamic interactions, however, the resulting identification algorithms are impractical, mainly due to their computational complexity. One of the alternatives offering fast identification algorithms is the block-oriented approach, in which systems of relatively simple structures are considered. The book provides nonparametric identification algorithms designed for such systems together with the description of their asymptotic and computational properties.

Inhalt

Introduction.- Hammerstein systems.- Identification goal.- Haar orthogonal bases.- Identification algorithms.- Computational algorithms. Final remarks. - Technical derivations.

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Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783642293955
    • Sprache Englisch
    • Auflage 2013
    • Größe H235mm x B155mm x T9mm
    • Jahr 2012
    • EAN 9783642293955
    • Format Kartonierter Einband
    • ISBN 3642293956
    • Veröffentlichung 12.10.2012
    • Titel Nonlinear System Identification by Haar Wavelets
    • Autor Przemys aw Sliwinski
    • Untertitel Lecture Notes in Statistics 210
    • Gewicht 242g
    • Herausgeber Springer Berlin Heidelberg
    • Anzahl Seiten 152
    • Lesemotiv Verstehen
    • Genre Mathematik

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