caa a22 4500 475833899 CHVBK 20180406123848.0 cr unu---uuuuu 170329e20000501xx s 000 0 eng 10.1007/s005210070036 doi (NATIONALLICENCE)springer-10.1007/s005210070036 Nauck D. Faculty of Computer Science (FIN-IWS), University of Magdeburg, Magdeburg, Germany, DE aut Adaptive Rule Weights in Neuro-Fuzzy Systems [Elektronische Daten] [D. Nauck] Neuro-fuzzy systems have recently gained a lot of interest in research and application. They are approaches that use learning techniques derived from neural networks to learn fuzzy systems from data. A very simple ad hoc approach to apply a learning algorithm to a fuzzy system is to use adaptive rule weights. In this paper, we argue that rule weights have a negative effect on the linguistic interpretation of a fuzzy system, and thus remove one of the key advantages for applying fuzzy systems. We show how rule weights can be equivalently replaced by modifying the fuzzy sets of a fuzzy system. If this is done, the actual effects that rule weights have on a fuzzy rule base become visible. We demonstrate at a simple example the problems of using rule weights. We suggest that neuro-fuzzy learning should be better implemented by algorithms that modify the fuzzy sets directly without using rule weights.: Springer-Verlag London Limited, 2000 Keywords: Fuzzy rule nationallicence Fuzzy system nationallicence Learning nationallicence Neural network nationallicence Neuro-fuzzy system nationallicence Rule weight nationallicence https://doi.org/10.1007/s005210070036 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s005210070036 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Nauck D. Faculty of Computer Science (FIN-IWS), University of Magdeburg, Magdeburg, Germany, DE aut Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer