Nonlinear System Identification by Haar Wavelets

Buch | Softcover
XI, 139 Seiten
2012 | 2013
Springer Berlin (Verlag)
978-3-642-29395-5 (ISBN)
53,49 inkl. MwSt
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.

Dr. Przemysław Śliwiński is an assistant professor at the Wrocław 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.

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

From the book reviews:

"This book is focused on identification algorithms, in particular, nonparametric identification algorithms for nonlinear dynamic systems. ... This book can be a good reference for people interested in the application of wavelets in a nonlinear system. The writing style of the book makes it easy to read." (Don Hong, Mathematical Reviews, July, 2014)

Erscheint lt. Verlag 12.10.2012
Reihe/Serie Lecture Notes in Statistics
Zusatzinfo XI, 139 p. 27 illus., 18 illus. in color.
Verlagsort Berlin
Sprache englisch
Maße 155 x 235 mm
Gewicht 248 g
Themenwelt Mathematik / Informatik Mathematik Wahrscheinlichkeit / Kombinatorik
Schlagworte Computational Algorithms • Haar bases • nonlinear approximation • nonlinear system identification • nonparametric algorithms • regression estimation
ISBN-10 3-642-29395-6 / 3642293956
ISBN-13 978-3-642-29395-5 / 9783642293955
Zustand Neuware
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