How the learning of rule weights affects the interpretability of fuzzy systems

Neuro-fuzzy systems have recently gained a lot of interest in research and applications. These are approaches that learn fuzzy systems from data. Many of them use rule weights for this task. In this paper we discuss the influence of rule weights on the interpretability of fuzzy systems. We show how rule weights can be equivalently replaced by modifications in the membership functions of a fuzzy system. We elucidate the effects rule weights have on a fuzzy rule base. Using our neuro-fuzzy model NEFCLASS we demonstrate the problems of using rule weights in a simple example, and we show that learning in fuzzy systems can be done without them