Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/93854
Type: Artigo de evento
Title: Mlp-based Equalization And Pre-distortion Using An Artificial Immune Network
Author: Attux R.R.D.F.
Duarte L.T.
Ferrari R.
Panazio C.M.
De Castro L.N.
Von Zuben F.J.
Romano J.M.T.
Abstract: Due to its universal approximation capability, the multilayer perceptron (MLP) neural network has been applied to several function approximation and classification tasks. Despite its success in solving these problems, its training, when performed by a gradient-based method, is sometimes hindered by the existence of unsatisfactory solutions (local minima). In order to overcome this difficulty, this paper proposes a novel approach to the training of a MLP based on a simple artificial immune network model. The application domain for assessing the performance of the proposed technique is that of digital communications, in particular, the problems of channel equalization and pre-distortion. The obtained simulation results demonstrate that the proposal is capable of efficiently solving the problems tackled. © 2005 IEEE.
Editor: 
Rights: fechado
Identifier DOI: 10.1109/MLSP.2005.1532895
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-33749074288&partnerID=40&md5=9c97494b40935b9297a60c5de89bf5fb
Date Issue: 2005
Appears in Collections:Unicamp - Artigos e Outros Documentos

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