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Learning of Fuzzy Membership Function by Novel Fuzzy-Neural Networks

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Journal of Korean Navigation and Port Reserch (한국항해항만학회지)
한국항해항만학회 (Korean Institute of Navigation and Port Research)
초록

Recently , there have been considerable researches about the fusion of fuzzy logic and neural networks. The propose of thise researches is to combine the advantages of both. After the function of approximation using GMDP (Generalized Multi-Denderite Product)neural network for defuzzification operation of fuzzy controller, a new fuzzy-neural network is proposed. Fuzzy membership function of the proposed fuzzy-neural network can be adjusted by learning in order to be adaptive to the variations of a parameter or the external environment. To show the applicability of the proposed fuzzy-nerual network, the proposed model is applied to a speed control o fDC sevo motor. By the hardware implementation, we obtained the desriable results.

저자
  • 추연규
  • 탁한호