Title :
Parameter tuning of fuzzy neural networks by immune algorithm
Author_Institution :
Dept. of Instrum. & Control Eng, Hanbat Nat. Univ., Seoul, South Korea
fDate :
6/24/1905 12:00:00 AM
Abstract :
Shows that auto tuning of membership functions and weights in fuzzy neural networks can be effectively performed by immune algorithms. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning methods, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights
Keywords :
feedforward neural nets; fuzzy neural nets; genetic algorithms; learning (artificial intelligence); tuning; auto tuning; fuzzy neural networks; genetic algorithms; hybrid methods; immune algorithm; learning methods; membership functions; near optimal fuzzy rules; optimal fuzzy rules; parameter tuning; weights; Automatic control; Control systems; Fuzzy control; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Genetic algorithms; Neural networks; Neurons; Uncertainty;
Conference_Titel :
Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7280-8
DOI :
10.1109/FUZZ.2002.1005025