| 基于粗集理论的归一化方法 |
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| 引用本文： | 庞清乐,孙同景,杨福刚,钟麦英.基于粗集理论的归一化方法[J].计算机工程,2007,33(8):36-38. |
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| 作者姓名： | 庞清乐 孙同景 杨福刚 钟麦英 |
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| 作者单位： | 1. 山东大学控制科学与工程学院,济南,250061;聊城大学物理系,聊城,252059
2. 山东大学控制科学与工程学院,济南,250061 |
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| 摘 要： | 针对神经网络分类器在不同类样本间距离较近时训练速度较慢的缺点，提出了基于粗集理论的归一化方法。利用粗集理论对样本进行归一化处理后，用处理后的样本对神经网络进行训练。并以配电网故障选线为例，对该方法进行了分析。仿真实验结果表明，样本处理后的神经网络训练时间明显缩短。
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| 关 键 词： | 归一化 粗集理论 神经网络 故障选线 |
| 文章编号： | 1000-3428（2007）08-0036-03 |
| 修稿时间： | 2006-04-22 |
Normalization Method Based on Rough Set Theory |
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| PANG Qingle,SUN Tongjing,YANG Fugang,ZHONG Maiying.Normalization Method Based on Rough Set Theory[J].Computer Engineering,2007,33(8):36-38. |
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| Authors: | PANG Qingle SUN Tongjing YANG Fugang ZHONG Maiying |
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| Affiliation: | 1. School of Control Science and Engineering, Shandong University, Jinan 25006 ; 2. Department of Physics, Liaocheng University, Liaocheng 252059 |
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| Abstract: | To overcome the disadvantage of the longtime training of neural network classifier when the distance between samples of different classes is small, the normalization method based on rough set theory is proposed. The samples are normalized using rough set theory and then the normalized samples are used to train neural network, The method is analyzed with an example of faulty line detection for distribution network. The simulation results show that the training time of neural network with processed samples is shorter. |
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| Keywords: | Normalization Rough set theory Neural network Faulty line detection |
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