A new on-line self-constructing neural fuzzy network
Andrés Ferreyra-Ramírez, José de Jesús Rubio · 2006
In this paper, we propose a new on-line self-constructing neural fuzzy network. Structure and parameter learning are updated at the same time in our algorithm, because there is no difference between them. It generates groups with a given radius. The center is updated in order to get a nearest one to the incoming data in each iteration, in this way, it does not generate many rules and it does not need to prune them. We give a time varying learning rate for backpropagation training. We use extended Kalman filter to train the center of sets in the THEN part. We proved the stability in both cases