DocumentCode :
424211
Title :
Fuzzy cognitive map learning based on improved nonlinear Hebbian rule
Author :
Li, Sheng-Jun ; Shen, Rui-Min
Author_Institution :
Dept. of Comput. Sci., Shanghai Jiao Tong Univ., China
Volume :
4
fYear :
2004
fDate :
26-29 Aug. 2004
Firstpage :
2301
Abstract :
Fuzzy cognitive map (FCM) is a powerful soft computing technique for modeling complex systems. It is a combination of fuzzy logic theory and neural networks. Developing of FCM is easy and adaptable based on human knowledge and experience. On the other hand, the main dependence on experts´ knowledge and opinion, and the potential convergence to undesire steady states are the shortcomings of FCMs. Learning methods are good choices used to overcome the shortcomings and strengthen the efficiency and robustness of FCM. This paper proposes one improved Hebbian algorithm on non-linear units for training FCMs. With the proposed learning procedure, FCM can modify its fuzzy causal web as casual pattern change and update their causal knowledge as experts.
Keywords :
Hebbian learning; fuzzy logic; fuzzy neural nets; large-scale systems; unsupervised learning; complex system; fuzzy cognitive map learning; fuzzy logic theory; fuzzy neural network; nonlinear Hebbian rule; soft computing technique; Convergence; Fuzzy cognitive maps; Fuzzy logic; Fuzzy systems; Humans; Learning systems; Neural networks; Power system modeling; Robustness; Steady-state;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
Type :
conf
DOI :
10.1109/ICMLC.2004.1382183
Filename :
1382183
Link To Document :
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