A learning algorithm for tuning fuzzy inference rules

In this paper, by using the gradient descent method we propose a tuning approach to obtain optimal fuzzy inference rules in which the membership functions are nonsymmetrical triangular-type membership functions. In the tuning approach, the representation of the fuzzy rule table does not change even after the learning which shows that it is intuitive and convenient for practical fuzzy applications. Moreover the efficiency of the presented method is also demonstrated by means of identifying nonlinear systems.