Hammerstein system identification by non-parametric regression estimation
作者:
WLODZIMIERZ GREBLICKI,
MTROSLAW PAWLAK,
期刊:
International Journal of Control
(Taylor Available online 1987)
卷期:
Volume 45,
issue 1
页码: 343-354
ISSN:0020-7179
年代: 1987
DOI:10.1080/00207178708933731
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
A discrete-time, multiple-input non-linear Hammerstein system is identified. The dynamical subsystem is recovered using the standard correlation method. The main results concern estimation of the non-linear memoryless subsystem. No conditions concerning the functional form of the transform characteristic of the subsystem are made and an algorithm for estimation of the characteristic is given. The algorithm is simply a non-parametric kernel estimate of the regression function calculated from the dependent data. It is shown that the algorithm converges to the characteristic of the subsystem in the pointwise as well as the global sense. For sufficiently smooth characteristics, the rate of convergence is o(n-1/(2+din probability, where d is the dimension of the input variable.
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