An enhanced approach for evolving participatory learning fuzzy modeling

Evolving participatory learning (ePL) modeling joins the concepts of participatory learning and evolving fuzzy systems. It uses data streams to continuously adapt the structure and functionality of fuzzy models. This paper proposes an enhanced version of the ePL approach, called ePL+, which includes both an utility measure as a mechanism to shrink rule bases, and a variable zone of influence of clusters. These features are useful in fuzzy rule-based modeling to construct the fuzzy rules. Computational experiments with the classic Mackey-Glass and Box & Jenkins benchmarks are conducted to compare the performance of the ePL+ approach with state of the art alternative fuzzy modeling methods and double exponential smoothing technique. The results show the high capability of ePL+ to model time series; it produces accurate results with a robust, flexible and autonomous algorithm.