Training Neurofuzzy Networks with Participatory Learning

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Author(s):	Hell, Michel ; Ballini, Rosangela ; Costa, Pyramo, Jr. ; Gomide, Fernando ; Stepnicka, M ; Novak, V ; Bodenhofer, U Total Authors: 7
Document type:	Journal article
Source:	NEW DIMENSIONS IN FUZZY LOGIC AND RELATED TECHNOLOGIES, VOL II, PROCEEDINGS; v. N/A, p. 2-pg., 2007-01-01.
Abstract
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)

FAPESP's process:	03/10019-9 - Forecasting methodologies for energy systems
Grantee:	Fernando Antonio Campos Gomide
Support Opportunities:	Research Projects - Thematic Grants