Title :
Fuzzy modeling based on generalized neural networks and fuzzy clustering objective functions
Author :
Sun, Chuen-Tsai ; Jang, Jyh-Shing
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA
Abstract :
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
Keywords :
Kalman filters; fuzzy control; fuzzy set theory; identification; learning (artificial intelligence); neural nets; I/O mapping; Kalman filter; fuzzy clustering objective functions; fuzzy if-then rules; fuzzy modelling; gradient-descent algorithm; learning procedure; membership functions; neural networks; Clustering algorithms; Density measurement; Fuzzy neural networks; Fuzzy systems; Humans; Inference algorithms; Input variables; Modeling; Neural networks; Partitioning algorithms; Sun;
Conference_Titel :
Decision and Control, 1991., Proceedings of the 30th IEEE Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-0450-0
DOI :
10.1109/CDC.1991.261075
