Abstract
In this paper, we develop a new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules by using training input–output data, based on the gradient descent method. A major advantage of this approach is that fuzzy rules or membership functions can be learned without changing the form of fuzzy rule table used in usual fuzzy applications, so that the case of non-firing or weak-firing can be well avoided, which is different from the conventional neuro-fuzzy learning algorithms. Moreover, some properties of the developed approach are also discussed. Finally, the efficiency of the developed approach is illustrated by means of identifying non-linear functions.
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