An efficient learning method of fuzzy inference system

One of the important problems to be solved for fuzzy inference systems is to tune the free parameters for solving the given task. In this paper, we propose to combine the RPROP adaptive learning algorithm, which is much faster than the gradient descent type, with the recursive-least-squares-error technique for tuning parameters of fuzzy membership functions. The work is based on the previous study of the adaptive network based fuzzy inference system (Jang (1993)). The proposed method is tested on several function approximation and dynamic system identification problems, and the results are compared with that of the gradient descent technique. The simulation results show improvements in learning speed and error convergence over the gradient-descant method.