How the Learning of Rule Weights A ects the Interpretability of Fuzzy Systems
نویسندگان
چکیده
Neuro-fuzzy systems have recently gained a lot of interest in research and application. These are approaches that learn fuzzy systems from data. Many of them use rule weights for this task. In this paper we discuss the innuence of rule weights on the interpretability of fuzzy systems. We show how rule weights can be equivalently replaced by modiications in the membership functions of a fuzzy system. By this we elucidate the eeects rule weights have on a fuzzy rule base. Using our neuro-fuzzy model NEFCLASS we demonstrate at a simple example the problems of using rule weights, and we show, that learning in fuzzy systems can be done without them.
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