NDLI: Wiener-NN models and robust identification

Content Provider	IEEE Xplore Digital Library
Author	Visala, A. Pitkanen, H. Paanajarvi, J.
Copyright Year	1999
Description	Author affiliation: Autom. & Technol. Lab., Helsinki Univ. of Technol., Espoo, Finland (Visala, A.)
Abstract	The robust identification principles of linear systems on the basis of n-width measure can be applied in dynamic nonlinear systems by using Wiener neural net (NN) structure. The reduced Wiener model consists of a cascade of Laguerre dynamics and static polynomial mapping. In Wiener-NN model the static nonlinear mapping is realized with NN. The space spanned by the continuous Laguerre functions is an optimal n-dimensional subspace in the n-width sense for a set of transfer functions having poles inside a certain disk by Wahlberg and Makila (1995). When a damped or slightly oscillating nonlinear system is linearized in all possible operating points, the corresponding poles define a certain closed set. If the disk referred above is parameterized so that it covers this set of poles, the corresponding Laguerre functions is an (heuristically) optimal n-dimensional subspace in the n-width sense for this nonlinear system in the Wiener context. Kautz functions form an (heuristically) optimal basis for a nonlinear dynamic system having dominating resonant mode. Chromatographic separation case is shortly demonstrated.
Starting Page	2188
Ending Page	2193
File Size	531816
Page Count	6
File Format	PDF
ISBN	0780355296
ISSN	10987576
DOI	10.1109/IJCNN.1999.832728
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Power system modeling Filter bank Nonlinear filters Transfer functions History Polynomials Neural networks Nonlinear equations Signal processing
Content Type	Text
Resource Type	Article

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