SciELO - Scientific Electronic Library Online

 
vol.14 número1Uma abordagem adaptativa de intervenção para mudança organizacionalRelacionamento colaborativo no canal de distribuição: uma matriz para análise índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

Compartilhar


Gestão & Produção

versão impressa ISSN 0104-530Xversão On-line ISSN 1806-9649

Resumo

COELHO, Leandro dos Santos; SILVA, Wesley Vieira da  e  PROTIL, Roberto Max. Nonlinear forecasting of eucalyptus wood prices based on an evolutionary neural network approach. Gest. Prod. [online]. 2007, vol.14, n.1, pp.139-154. ISSN 1806-9649.  https://doi.org/10.1590/S0104-530X2007000100012.

Computational tools of system identification and prediction of time series allows for the conception of mathematical models based on numerical data. The key problem in these cases is to find a suitable mathematical model. This paper presents a radial basis function neural network (RBF-NN) design for forecasting time series. Using the RBF-NN for nonlinear system forecasting is quite difficult as one has to choose an appropriate set of centers and spreads for the Gaussian activation functions to achieve a good network structure. In this work, the setup of RBF-NN is based on a hybrid method based on the Gustafson-Kessel clustering method and optimization procedure by differential evolution. The RBF-NN design is validated for the one-step ahead forecasting of eucalyptus wood prices for cellulose and sawmill to illustrate the effectiveness of this hybrid approach. The performance of the RBF-NN design based on forecasting results is presented and discussed in details.

Palavras-chave : Time series; Forecasting; Neural network; Radial basis function; Differential evolution.

        · resumo em Português     · texto em Português     · Português ( pdf )

 

Creative Commons License Todo o conteúdo deste periódico, exceto onde está identificado, está licenciado sob uma Licença Creative Commons