A New Approach of Neurofuzzy Learning Algorithm
In this chapter, we develop a new approach of neurofuzzy learning algorithm for tuning fuzzy rules by using training input-output data, based on the gradient descent method. The 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 weak-firing can be well avoided, which is different from the conventional neurofuzzy learning algorithms. Moreover, we show the efficiency of the developed method by identifying nonlinear functions.
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