Please use this identifier to cite or link to this item:
Type: Artigo de evento
Title: Uninetworks In Time Series Forecasting
Author: Hell M.
Gomide F.
Ballini R.
Costa Jr. P.
Abstract: This paper presents an approach for time series forecasting using a new class of fuzzy neural networks called uninetworks. Uninetworks are constructed using a recent generalization of the classic and and or logic neurons. These generalized logic neurons, called unineurons, provide a mechanism to implement general nonlinear processing and introduce important characteristics of biological neurons such as neuronal AND synaptic plasticity. Unineurons achieve synaptic and neuronal plasticity modifying their internal parameters in response to external changes. Thus, unineurons may individually vary from an and neuron to an or neuron (and vice-versa), depending upon the necessity of the modeling task. Besides, the proposed neural fuzzy networks are able to extract knowledge from input/output data and to encode it explicitly in the form of if-then rules. Therefore, linguistic models are obtained in a form suitable for human understanding. Experimental results show that the models proposed here are more general and perform best in terms of accuracy and computational costs when compared against alternative approaches suggested in the literature. ©2009 IEEE.
Rights: fechado
Identifier DOI: 10.1109/NAFIPS.2009.5156424
Date Issue: 2009
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-70350410105.pdf525.06 kBAdobe PDFView/Open

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