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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|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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