| 基于粗糙集理论的模糊神经网络建模方法 |
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| 引用本文： | 王玲玲,申东日,陈义俊,祁军.基于粗糙集理论的模糊神经网络建模方法[J].桂林电子科技大学学报,2006,26(5):366-369. |
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| 作者姓名： | 王玲玲 申东日 陈义俊 祁军 |
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| 作者单位： | 辽宁石油化工大学,信息与控制工程学院,辽宁,抚顺,113001 |
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| 摘 要： | 采用基于粗糙集的模糊神经网络模型,将粗糙集理论与模糊神经网络相结合,通过利用粗糙集理论中的约简的计算方法,从样本数据中获取精简的规则,再根据这些规则构造模糊神经网络各层的神经元个数,克服了当输入维数高时,模糊神经网络的结构过于庞大的缺点,从而使网络模型结构最简.并采用误差反向传播算法(BP算法)来训练该新型网络中的权值参数及隶属函数的中心值和宽度,仿真结果验证了该模型的优越性.
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| 关 键 词： | 粗糙集 模糊神经网络 约简 规则获取 BP算法 |
| 文章编号： | 1673-808X（2006）05-0366-04 |
| 收稿时间： | 2006-09-13 |
| 修稿时间： | 2006-09-13 |
Modeling method of fuzzy neural network based on rough set theory |
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| WANG Ling-ling,SHEN Dong-ri,CHEN Yi-jun,QI Jun.Modeling method of fuzzy neural network based on rough set theory[J].Journal of Guilin Institute of Electronic Technology,2006,26(5):366-369. |
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| Authors: | WANG Ling-ling SHEN Dong-ri CHEN Yi-jun QI Jun |
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| Affiliation: | Information and Control Engineering Institute, Liaoning University of Petroleum and Chemistry Technology, Fushun 113001, China |
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| Abstract: | A fuzzy neural network model based on rough set theory is proposed by integrating rough set with fuzzy neural network.The method of reduction from the rough set theory is used first to obtain reductive rules from swatch data.These rules are then exploited to construct neural cell numbers in fuzzy neural network.The shortcomings of the input dimension and the excessively large structure are thus overcome,which enables the structure to become the simplest.Finally,the parameter values of the network is trained by error back propagation(BP)algorithm.The results of simulation programmed by MATLAB have confirmed the advantages of the model. |
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| Keywords: | rough set fuzzy neural network reduction rule acquisition BP algorithm |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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