Training Neurofuzzy Networks with Participatory Learning

Autor(es)

Hell, Michel ; Ballini, Rosangela ; Costa, Pyramo, Jr. ; Gomide, Fernando ; Stepnicka, M ; Novak, V ; Bodenhofer, U

• Tipo de documento: Artigo Científico
• Fonte: NEW DIMENSIONS IN FUZZY LOGIC AND RELATED TECHNOLOGIES, VOL II, PROCEEDINGS; v. N/A, p. 2-pg., 2007-01-01.

Resumo

This paper introduces a new approach to adjust a class of neurofuzzy networks based on the idea of participatory learning. Participatory learning is a mean to learn and revise beliefs based on what is already known or believed. The performance of the approach is verified with the Box and Jenkins gas furnace modeling problem, and with a short-term load forecasting problem using actual data. Comparisons with alternative training procedures suggested in the literature are included to shown the effectiveness of participatory learning to train neurofuzzy networks. (AU)

• Processo FAPESP: 03/10019-9 - Metodologias de previsão para sistemas de energia
• Beneficiário: Fernando Antonio Campos Gomide
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 03/05042-1 - Abordagem neurofuzzy para modelagem de sistemas dinamicos nao lineares.
• Beneficiário: Michel Bortolini Hell
• Modalidade de apoio: Bolsas no Brasil - Doutorado