Application of Levenberg-Marquardt Algorithm to Training of T-S Fuzzy Model Based RBF Neural Network
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    Abstract:

    To improve the efficiency of training the T-S fuzzy model based RBF neural network, the Levenberg-Marquardt algorithm is introduced into it, which speeds up the convergence and reduces the probability for the training to get into the local minimum point. Next, a kind of more efficient algorithm, named hybrid learning algorithm,is proposed. At last, the efficiency and practicability of the Levenberg-Marquardt algorithm for the training of the T-S fuzzy model based RBF neural network are tested through an experiment.

    Reference
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    3 鲍鸿,黄心汉,李锡雄. T-S型模糊RBF神经网络的结构研究.华南理工大学学报, 1999,27(1):11-13.
    4 赵恒平, 俞金寿. 一种基于T-S模糊模型的自适应建模方法及其应用. 华东理工大学学报, 2004, 30(4):442-445.
    5 李战明,王君,康爱红. 基于T-S模糊模型的RBF网络的自适应学习算法. 兰州理工大学学报, 2004,30(2) 82-85.
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    7 Hagan MT, Menhaj MB. Trainning Feedforward Networks With the Marquardt Algorithm. IEEE Transactions on Neural Networks, 1994,5(6):989-993.
    8 Jang JSR. ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Trans. Systems, Man, and Cybernetics, 1993,23(3):665-685.
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徐奉友,张小刚. Levenberg-Marquardt算法在T-S型模糊RBF神经网络训练中的应用.计算机系统应用,2010,19(12):155-159

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  • Received:April 14,2010
  • Revised:June 04,2010
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