[1]冯远静,李良福,冯祖仁.粗糙集CMAC神经网络故障诊断策略[J].华侨大学学报(自然科学版),2004,25(3):318-321.[doi:10.3969/j.issn.1000-5013.2004.03.023]
　Feng Yuanjing,Li Liangfu,Feng Zuren.Rough Set-Based CMAC Neural Network for Fault Diagrosis[J].Journal of Huaqiao University(Natural Science),2004,25(3):318-321.[doi:10.3969/j.issn.1000-5013.2004.03.023]
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粗糙集CMAC神经网络故障诊断策略()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第25卷
期数:
2004年第3期
页码:
318-321
栏目:
出版日期:
2004-07-20

文章信息/Info

Title:
Rough Set-Based CMAC Neural Network for Fault Diagrosis
文章编号:
1000-5013(2004)03-0318-04
作者:
冯远静; 李良福; 冯祖仁
西安交通大学系统工程研究所; 西安交通大学系统工程研究所 陕西西安710049; 陕西西安710049; 陕西西安710049
Author(s):
Feng Yuanjing;  Li Liangfu;  Feng Zuren
Inst. of System Eng., Xi’an Jiaotong Univ., 710049, Xi’an, China
关键词:
粗糙集;  神经网络;  故障诊断;  变压器
Keywords:
rough set;  neural network;  fault diagnosis;  transformer
分类号:
TP183
DOI:
10.3969/j.issn.1000-5013.2004.03.023
文献标志码:
A
摘要:
提出一种基于粗糙集CMAC神经网络的智能互补融合的诊断策略 .该策略利用粗糙集理论对数据样本进行数据浓缩,提取初步的诊断规则 .对初步的诊断规则通过神经网络进行粗映射,利用神经网络的分类逼近能力,建立故障状态空间到诊断空间的精确映射 .大大提高了神经网络的收敛速度和逼近精度 .将该神经网络应用于的变压器故障诊断实例,结果表明,该神经网络具有分类逼近能力强,计算量小等优点 .诊断正确率比普通神经网络的诊断正确率高
Abstract:
A rough set based CMAC neural network is put forward as intelligent complementary and blending tactics of diagnosis. This tactics carry out data compaction on data samples and extract initial diagnostic rule by using rough set theory. To carry out rough mapping on the initial diagnostic rule through neural network and to use the sort approximation ability of neural network, an exact mapping from space of fault state to space of diagnosis is established by which convergence rate and approximation accuracy are greatly improved. This neural network is applied to the example of fault diagnosis of transformer. The result shows that the neural network is strong in sort approximation ability and small in workload of computation and high in rate of correct diagnosis, as compared with that of conventional neural network.

参考文献/References:

[1] Chen Anpin, Lin Changchun. Fuzzy approaches for fault diagnosis of transformers [J]. Fuzzy Sets and Systems, 2001(1):139-151.doi:10.1016/S0165-0114(99)00115-3.
[2] 束洪春, 孙向飞, 司大军. 电力变压器故障诊断专家系统知识库建立和维护的粗糙集方法 [J]. 中国电机工程学报, 2002(2):31-35.doi:10.3321/j.issn:0258-8013.2002.02.007.
[3] Zhang Y. An artificial new network approach to transformer fault diagnosis [J]. IEEE Transactions on Power Delivery, 1996(4):1836-1841.
[4] Lin Weisong, Hung Chinpao, Wang Manghui. CMAC-based fault diagnosis of power transformers [J]. Proceedings of the International Joint Conference on Neural Networks, 2002(1):986-991.
[5] 王源, 胡寿松, 齐俊伟. 自组织模糊CMAC神经网络及其非线性系统辨识 [J]. 航空学报, 2001(6):556-558.doi:10.3321/j.issn:1000-6893.2001.06.018.
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备注/Memo

备注/Memo:
国家自然科学基金资助项目(60175015)
更新日期/Last Update: 2014-03-23