Fuzzy modeling based on generalized neural networks and fuzzy clustering objective functions
Chang Sun, Jyh‐Shing Roger Jang · 2002
An approach to the formulation of fuzzy if-then rules based on clustering objective functions is proposed. The membership functions are then calibrated with the generalized neural networks technique to achieve a desired input-output mapping. The learning procedure is basically a gradient-descent algorithm. A Kalman filter algorithm is used to improve the overall performance.>