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LI Kan, LIU Yu-shu. Rough Set Based Fuzzy Neural Network for Pattern Classification[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2003, 12(4): 428-431.
Citation: LI Kan, LIU Yu-shu. Rough Set Based Fuzzy Neural Network for Pattern Classification[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2003, 12(4): 428-431.
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Rough Set Based Fuzzy Neural Network for Pattern Classification

  • Department of Computer Science and Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China

Funds:theMinisterialLevelAdvancedResearchFoundation(2040505)
  • Received Date:2003-05-30
    Abstract
  • A rough set based fuzzy neural network algorithm is proposed to solve the problem of pattern recognition. The least square algorithm (LSA) is used in the learning process of fuzzy neural network to obtain the performance of global convergence. In addition, the numbers of rules and the initial weights and structure of fuzzy neural networks are difficult to determine. Here rough sets are introduced to decide the numbers of rules and original weights. Finally, experiment results show the algorithm may get better effect than the BP algorithm.
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