ENGLISH

Nonparametric system identification

By Wlodzimierz Greblicki , Miroslaw Pawlak

Book information

Publisher
Cambridge University Press
Year
2008
ISBN
9780521868044, 0521868041
LCC
QA402 .G7315 2008
Open Library ID
OL10437761M
Language
english
Format
PDF
Filesize
6 MB (6429284 bytes)
Edition
draft
Pages
319\319
Library
Kolxo3
Time added
2009-07-20 03:45:11

Description

Presenting a thorough overview of the theoretical foundations of non-parametric system identification for nonlinear block-oriented systems, this books 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.

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