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Type: Artigo de evento
Title: Application Of The Hopfield Network In Robust Estimation Of Parametric Membership Sets For Linear Models
Author: da Silva Ivan N.
de Arruda Lucia Valeria R.
do Amaral Wagner C.
Abstract: High computation rates can be achieved using artificial neural networks. Optimization problems can be solved by neural networks with feedback connections by employing a massive number of simple processing elements with high degree of connectivity between these elements. In this paper, an application of Hopfield neural networks in Robust Parametric Estimation with unknown-but-bounded disturbance is presented. The internal parameters of the Hopfield neural network are obtained using the valid-subspace technique. These parameters are explicitly computed to assure the network convergence. A comparative analysis with other robust estimation methods is carried out by a simulation example.
Editor: IEEE, Piscataway, NJ, United States
Rights: fechado
Identifier DOI: 
Date Issue: 1995
Appears in Collections:Unicamp - Artigos e Outros Documentos

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