Adaptive Rule Weights in Neuro-Fuzzy Systems |
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Authors: | D Nauck |
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Affiliation: | (1) Faculty of Computer Science (FIN-IWS), University of Magdeburg, Magdeburg, Germany, DE |
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Abstract: | 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. |
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Keywords: | : Fuzzy rule Fuzzy system Learning Neural network Neuro-fuzzy system Rule weight |
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