| Rough Set Based Fuzzy Neural Network for Pattern Classification |
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| 作者姓名： | 李侃 刘玉树 |
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| 作者单位： | DepartmentofComputerScienceandEngineering,SchoolofInformationScienceandTechnology,BeijingInstituteofTechnology,Beijing100081,China |
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| 基金项目： | theMinisterialLevelAdvancedResearchFoundation ( 2 0 40 5 0 5 ) |
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| 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.
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| 关 键 词： | 粗糙集 模糊神经网络 最小平方算法 模型分类 BP算法 |
| 收稿时间： | 2003/5/30 0:00:00 |
Rough Set Based Fuzzy Neural Network for Pattern Classification |
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| LI Kan and 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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| Authors: | LI Kan and LIU Yu-shu |
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| Affiliation: | Department of Computer Science and Engineering, School of Information Science and Technology, Beijing Institute ofTechnology, Beijing 100081, China |
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| 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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| Keywords: | fuzzy neural network rough sets the least square algorithm back-propagation algorithm |
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