Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/71163
Type: Artigo de periódico
Title: Robust estimation of parametric membership regions using artificial neural networks
Author: daSilva, IN
deArruda, LVR
doAmaral, WC
Abstract: This paper is concerned with the robust identification of linear models when modelling error is bounded. A modified Hopfield's neural network is used to calculate a membership set for the model parameters, with the internal parameters of the network obtained using the valid-subspace technique. These parameters can be explicitly computed to guarantee the network convergence. A solution for the robust estimation problem with an unknown-but-bounded error corresponds to an equilibrium point of the network. A comparative analysis with alternative robust estimation methods is provided to illustrate the proposed approach.
Country: Inglaterra
Editor: Taylor & Francis Ltd
Rights: fechado
Date Issue: 1997
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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