Please use this identifier to cite or link to this item:
Type: Artigo de periódico
Title: A fast learning algorithm for evolving neo-fuzzy neuron
Author: Silva, AM
Caminhas, W
Lemos, A
Gomide, F
Abstract: This paper introduces an evolving neural fuzzy modeling approach constructed upon the neo-fuzzy neuron and network. The approach uses an incremental learning scheme to simultaneously granulatethe input space and update the neural network weights. The neural network structure and parameters evolve simultaneously as data are input. Initially the space of each input variable is granulated using two complementary triangular membership functions. New triangular membership functions may be added, excluded and/or have their parameters adjusted depending on the input data and modeling error. The parameters of the network are updated using a gradient-based scheme with optimal learning rate. The performance of the approach is evaluated using instances of times series forecasting and nonlinear system identification problems. Computational experiments and comparisons against alternative evolving models show that the evolving neural neo-fuzzy network is accurate and fast, characteristics which are essential for adaptive systems modeling, especially in real-time, on-line environments. (C) 2013 Elsevier B. V. All rights reserved.
Subject: Evolving neural fuzzy systems
Neo-fuzzy neuron
Adaptive modeling
Country: Holanda
Editor: Elsevier Science Bv
Citation: Applied Soft Computing. Elsevier Science Bv, v. 14, n. 194, n. 209, 2014.
Rights: fechado
Identifier DOI: 10.1016/j.asoc.2013.03.022
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
There are no files associated with this item.

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