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Volume 23 Issue 4
Apr.  2001
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Li Ying, Bai Bendu, Jiao Licheng. An Adaptive Fuzzy Neural Network for Identification of the Complicated Noulinear System[J]. Journal of Electronics & Information Technology, 2001, 23(4): 332-337.
Citation: Li Ying, Bai Bendu, Jiao Licheng. An Adaptive Fuzzy Neural Network for Identification of the Complicated Noulinear System[J]. Journal of Electronics & Information Technology, 2001, 23(4): 332-337.

An Adaptive Fuzzy Neural Network for Identification of the Complicated Noulinear System

  • Received Date: 1999-06-24
  • Rev Recd Date: 1999-11-19
  • Publish Date: 2001-04-19
  • This paper presents a compound neural network model, i.e., adaptive fuzzy neural network (AFNN), which can be used for identifying the complicated nonlinear system. AFNN has a simple structure and possesses the ability of universal approximation. It is capable of overcoming the error of system identification due to the existence of some changing points and improving the accuracy of identification of the whole system. The effectiveness of the model is tested on the identification result of missile attacking area.
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  • K.S. Narendra, K. Parthasarathy, Identification and control of dynamical systems using neural network, IEEE Trans. on Neural Network, 1990, 1(1), 4-27.[2]K.J. Hunt, Neural networks for control systems-A survey, IEEE Trans. on Neural Networks, 1993, 3(5), 752-760.[3]J.C. Bezdek, Pattern recognition with fuzzy objection function algorithms, New York, Plenum, 1981, Ch.3. [4]L.X. Wang, M. Mendel, Fuzzy basis function, universal approximation, and orthogonal leastsquares learning, IEEE Trans. on Neural Network, 1992, 3(5), 807-814.[4]Li Rui-Ping, M. Mukaidono, Fuzzy modeling and clustering neural network, Control and Cyber netics, 1996, 25(2), 225-242.[5]A. Bastian, Sequential fuzzy system identification, Control and Cybernetics, 1996, 25(2), 199-223.[6]A. Bastian, An effective way to generate neural network structures for function approximation, Mathware, 1994, 1(1), 139-161.[7]P.J. Angeline, et al., An evolutionary algorithm that construct recurrent neural networks, IEEE Trans. on Neural Network, 1994, 5(1), 39-53.
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