Abstract
This paper proposes a self-tuning method for fuzzy if-then rule generation based on neural network and clustering algorithm techniques. In the tuning approach, the initial parameters of fuzzy rules are roughly designed by using a fuzzy clustering algorithm, and then fuzzy rules under fuzzy singleton-type reasoning are tuned by using a neuro-fuzzy learning algorithm. By this approach, the learning time can be reduced and the generated fuzzy rules are reasonable and suitable for the identified system. Finally, identifying a nonlinear function shows the efficiency of the tuning approach.
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