New neurofuzzy training procedure based on participatory learning paradigm

In this paper we introduce a new approach to train neurofuzzy networks using the participatory learning concept. The participatory learning paradigm tends to emulate the human learning mechanism where an acceptance mechanism determines which observation is used for learning based upon their compatibility with the current beliefs. The performance of the proposed learning scheme is illustrated by an example involving a nonlinear system modeling problem: the thermal modeling of power transformers. Comparisons with other methods reported in the literature and between two dual network structures are also included. The experimental results show the effectiveness of participatory learning in neurofuzzy networks training.