Rough Set Based Fuzzy Neural Network for Pattern Classification

被引：0
|
作者：
李侃
刘玉树
机构：
[1] Beijing Institute of Technology
[2] Beijing100081
[3] China
[4] Department of Computer Science and Engineering
[5] Department of Computer Science and Engineering School of Information Science and Technology
来源：
Journal of Beijing Institute of Technology(English Edition) | 2003年 / 04期
关键词：
fuzzy neural network; rough sets; the least square algorithm; back-propagation algorithm;
D O I：
10.15918/j.jbit1004-0579.2003.04.021
中图分类号：
TP183 [人工神经网络与计算];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：428 / 431
页数：4
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