A learning algorithm for tuning fuzzy rules based on the gradient descent method

In this paper, we suggest a utility learning algorithm for tuning fuzzy rules by using input-output training data, based on the gradient descent method. The major advantage of this method is that the fuzzy rules or membership functions can be learned without changing the form of the fuzzy rule table used in usual fuzzy controls, so that the case of weak-firing can be avoided, which is different from the conventional learning algorithm. Furthermore, we illustrated the efficiency of the suggested learning algorithm by means of several numerical examples.