Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/90757
Type: Artigo de evento
Title: Evolving Neural Fuzzy Network With Adaptive Feature Selection
Author: Silva A.M.
Caminhas W.M.
Lemos A.P.
Gomide F.
Abstract: This paper introduces a neural fuzzy network approach for evolving system modeling. The approach uses neofuzzy neurons and a neural fuzzy structure monished with an incremental learning algorithm that includes adaptive feature selection. The feature selection mechanism starts considering one or more input variables from a given set of variables, and decides if a new variable should be added, or if an existing variable should be excluded or kept as an input. The decision process uses statistical tests and information about the current model performance. The incremental learning scheme simultaneously selects the input variables and updates the neural network weights. The weights are adjusted using a gradient-based scheme with optimal learning rate. The performance of the models obtained with the neural fuzzy modeling approach is evaluated considering weather temperature forecasting problems. Computational results show that the approach is competitive with alternatives reported in the literature, especially in on-line modeling situations where processing time and learning are critical. © 2012 IEEE.
Editor: 
Rights: fechado
Identifier DOI: 10.1109/ICMLA.2012.184
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84873580279&partnerID=40&md5=90d7ec23fa7f8ce7e5f383766c59b3a6
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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