NDLI: Robust nonlinear system identification using neural network models

Content Provider	IEEE Xplore Digital Library
Author	Sonwu Lu Basar, T.
Copyright Year	1995
Description	Author affiliation: Coordinated Sci. Lab., Illinois Univ., Urbana, IL, USA (Sonwu Lu; Basar, T.)
Abstract	Studies the problem of identification for nonlinear systems in the presence of unknown driving noise, using both feedforward multilayer neural network and radial basis function network models. The difficulty associated with the persistency of excitation condition (inherent to the standard schemes in the neural identification literature) is circumvented here by a novel formulation and by using a new class of identification algorithms. By embedding the original problem in one with noise-perturbed state measurements, the authors present a class of identifiers (under /spl Lscr//sub 1/ and /spl Lscr//sub 2/ cost criteria) which secure a good approximant for the system nonlinearity provided that some global optimization technique is used. For one special network structure, viz. the RBF network, the authors present a neural network version of an H/sup /spl infin//-based identification algorithm, and show how, along with an appropriate choice of control input to enhance excitation, under both full-state-derivative information and noise-perturbed full-state information, it leads to satisfaction of a relevant persistency of excitation condition, and thereby to robust identification of the system nonlinearity.
Starting Page	1840
Ending Page	1845
File Size	656129
Page Count	6
File Format	PDF
ISBN	0780326857
ISSN	01912216
DOI	10.1109/CDC.1995.480609
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-12-13
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear systems Neural networks Noise robustness Multi-layer neural network Radial basis function networks Feedforward neural networks Noise measurement Cost function Nonlinear control systems Control systems
Content Type	Text
Resource Type	Article

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