Application of Levenberg-Marquardt Algorithm to Training of T-S Fuzzy Model Based RBF Neural Network
DOI:
CSTR:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    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
    Related
    Cited by
Get Citation

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

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:April 14,2010
  • Revised:June 04,2010
  • Adopted:
  • Online:
  • Published:
Article QR Code
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063