NDLI: Identification of MISO nonlinear systems via the semiparametric approach

Content Provider	IEEE Xplore Digital Library
Author	Jiaqing Lv Pawlak, M.
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, R3T5V6, Canada (Jiaqing Lv; Pawlak, M.)
Abstract	In this paper we examine a class of multiple-input, single-output (MISO) nonlinear systems of the block-oriented structure. In particular, we focus on MISO Hammerstein systems being the cascade connection of a multivariate nonlinearity with a linear dynamical subsystem. In order to alleviate an apparent curse of dimensionality occurring in the problem of estimating the nonlinearity, we propose to a semi-parametric strategy for identification of the nonlinear system. This is carried out by projecting the d-dimensional input signal onto one dimensional subset which, in turn, is mapped by a univariate nonparametric function to an internal unobserved signal of the system. Such a parsimonious representation allows us to overcome the curse of dimensionality as the accuracy of our identification algorithms is independent of d. We identify the system via the semi-parametric version of the least-squares. The statistical accuracy of the resulting estimates is obtained via the theory of M- estimation. These theoretical findings are verified in numerous simulation studies.
Starting Page	4084
Ending Page	4087
File Size	543137
Page Count	4
File Format	PDF
ISBN	9781457705380
ISSN	15206149
e-ISBN	9781457705397
e-ISBN	9781457705373
DOI	10.1109/ICASSP.2011.5947250
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-22
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Kernel Estimation Bandwidth Accuracy Nonlinear systems Approximation methods semi-parametric inference multivariate nonlinear systems MISO Hammerstein systems curse of dimensionality
Content Type	Text
Resource Type	Article

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