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|Type:||Artigo de periódico|
|Title:||Robust estimation of parametric membership regions using artificial neural networks|
|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.|
|Editor:||Taylor & Francis Ltd|
|Appears in Collections:||Artigos e Materiais de Revistas Científicas - Unicamp|
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