Evolving Fuzzy Modeling Using Participatory Learning
Estela de Oliveira Lima, Michel Hell, Rosângela Ballini, Fernando A. C. Gomide · 2010
This chapter introduces an approach to developing evolving fuzzy rule-based models using participatory learning. Participatory learning assumes that learning and beliefs about a system depend on what the learning mechanism knows about the system itself. The basic learning mechanisms rely on online clustering and parameter estimation using least squares algorithms. Evolving fuzzy participatory learning (ePL) modeling adopts the same scheme as evolving Takagi-Sugeno (eTS) modeling. After the initialization phase, data processing is performed at each step to verify whether a new cluster must be created, an old cluster should be modified to account for the new data, or redundant clusters must be eliminated. Computational experiments with a classic benchmark problem and a real-world application concerning hourly electrical load forecasting show that ePL is a promising approach in the adaptive fuzzy systems modeling area. Controlled Vocabulary Terms fuzzy systems; learning systems; load forecasting; workstation clusters