Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/104127
Type: Artigo de evento
Title: Nullneurons-based Hybrid Neurofuzzy Network
Author: Hell M.
Costa Jr. P.
Gomide F.
Abstract: In this paper we introduce design and learning schemes for hybrid neurofuzzy networks based on nullneurons. A nullneuron is a logic neuron that performs an operation ψ parameterized by u (absorbing element). The nullneuron becomes a AND neuron if u = 0 and a dual OR neuron if u = 1. The operator ψ is a composition of nullnorms. Based on input-output data, the learning procedure proposed here adjusts not only the weights associated with the individual inputs of the nullneurons, but also the type of the nullneuron in the network (AND or OR) learning the value of parameter u. Adjustment of u is done individually and after learning each nullneuron can be either a AND neuron or a OR neuron, independently of the state of the remaining nullneurons. Consequently, the neurofuzzy network presented in this paper is more general than alternative approaches discussed in the literature because it embeds a set of if-then rules that uses different connectives in their antecedents. Experimental results are included to show that the neurofuzzy network proposed provides accurate models after short period of learning time. © 2007 IEEE.
Editor: 
Rights: fechado
Identifier DOI: 10.1109/NAFIPS.2007.383860
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-35148886301&partnerID=40&md5=6ee83bb01e248246d5bbe2f919b053e1
Date Issue: 2007
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-35148886301.pdf380.67 kBAdobe PDFView/Open


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