Title :
A comparative study of learning methods in tuning parameters of fuzzy membership functions
Author_Institution :
Dept. of Electr. Eng., Da-Yeh Univ., Chang-Hwa, Taiwan
fDate :
6/21/1905 12:00:00 AM
Abstract :
We compare several popular training algorithms for tuning parameters of fuzzy membership functions (MFs). The algorithms compared are gradient descent (GD), resilient propagation (RPROP), Quickprop (QP), and Levenberg-Marquardt (LM) algorithms. These algorithms are combined with RLSE (recursive least squares estimate) to improve the efficiency of an ANFIS (adaptive network-based fuzzy inference system). The results, on average, show that the relative performance of these algorithms depends on the given task, but that RPROP produces better performance in terms of convergence speed, stability, and generalization properties
Keywords :
convergence; fuzzy logic; fuzzy set theory; inference mechanisms; learning (artificial intelligence); least squares approximations; neural nets; parameter estimation; tuning; ANFIS; Levenberg-Marquardt algorithms; Quickprop; adaptive network-based fuzzy inference system; fuzzy membership functions; gradient descent; learning methods; recursive least squares estimate; resilient propagation; Adaptive systems; Convergence; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Learning systems; Least squares approximation; Quadratic programming; Recursive estimation; Stability;
Conference_Titel :
Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
Conference_Location :
Tokyo
Print_ISBN :
0-7803-5731-0
DOI :
10.1109/ICSMC.1999.823150
