| 基于粗糙集和神经网络的润滑油中磨损磨粒的识别 |
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| 引用本文： | 杨宏伟,钟新辉,胡建强. 基于粗糙集和神经网络的润滑油中磨损磨粒的识别[J]. 润滑与密封, 2007, 32(1): 162-164 |
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| 作者姓名： | 杨宏伟 钟新辉 胡建强 |
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| 作者单位： | 徐州空军学院,江苏徐州,221006;徐州空军学院,江苏徐州,221006;徐州空军学院,江苏徐州,221006 |
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| 摘 要： | 为了更有效地对润滑油中的磨损磨粒进行识别,探讨了基于粗糙集和神经网络的磨粒识别。它首先利用粗糙集理论对磨粒特征参数进行约简,这样能够大大减少了神经网络的输入维数。然后介绍了一种径向基神经网络,并利用它对磨粒进行分类。对20个磨粒进行识别,磨粒分类分对14个,分错6个,识别率达到70.0%。
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| 关 键 词： | 模式识别 磨损磨粒 径向基神经网络 粗糙集 |
| 文章编号： | 0254-0150（2007）1-162-3 |
| 修稿时间： | 2006-04-06 |
Recognition of Wear Debris in Lubricating Oil Based on Rough Set and Neural Network |
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| Yang Hongwei,Zhong Xinhui,Hu Jianqiang. Recognition of Wear Debris in Lubricating Oil Based on Rough Set and Neural Network[J]. Lubrication Engineering, 2007, 32(1): 162-164 |
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| Authors: | Yang Hongwei Zhong Xinhui Hu Jianqiang |
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| Affiliation: | Xuzhou Air Force Institute, Xuzhou Jiangsu 221006, China |
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| Abstract: | In order to classify wear debris in lubricating oil more effectively,a wear debris recognition method based on rough set and neural network was put forward.At first,debris feature parameters are simplified based on rough sets theory,and the input information-dimensions is reduced.Then a radius basis function neural network was introduced,and it is used to classify wear debris.20 wear debris was classified by the method,and result shows that 14 is right,6 is fault,the ratio of recognition reaches 70.0%. |
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| Keywords: | pattern recognition wear debris RBF neural network rough set |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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