Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/95917
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: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-0029482639&partnerID=40&md5=aa3413b3ae575499ef3986d163ea4c54
Date Issue: 1995
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-0029482639.pdf475.33 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.