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- W2357805956 abstract "Radial basis function networks and fuzzy rule systems are functionally equivalent under some mild conditions. Therefore the learning algorithms developed in the field of neural networks can be used to adjust the parameters of fuzzy systems.The learned fuzzy systems are self-study and self-organize,but weaken their interpretability.Combining the fuzzy decision theory with the neural networks,and analyze an improved RBF neural network structure.This fuzzy neural network can express the interpretability of fuzzy systems,which is considered to be one of the most important features of fuzzy systems,and make fuzzy systems self-study and self-organize.Based on VC++ programming,we have achieved the algorithm of extracting fuzzy rules,and experimented.The results of simulation example show that the algorithm is an effective method." @default.
- W2357805956 created "2016-06-24" @default.
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- W2357805956 date "2005-01-01" @default.
- W2357805956 modified "2023-09-26" @default.
- W2357805956 title "Achievement of an Algorithm for Extracting Fuzzy Rules Based on RBF Neural Network" @default.
- W2357805956 hasPublicationYear "2005" @default.
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