| 子网络级模拟故障诊断的神经网络实现 |
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| 引用本文： | 刘利强,李春明. 子网络级模拟故障诊断的神经网络实现[J]. 现代电子技术, 2007, 30(8): 118-120 |
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| 作者姓名： | 刘利强 李春明 |
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| 作者单位： | 内蒙古工业大学,信息工程学院,内蒙古,呼和浩特,010062 |
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| 摘 要： | 利用容差模拟电路节点电压灵敏度序列守恒定理,得到了模拟电路元件的软、硬故障统一样本。然后利用统一样本集训练BP神经网络,并将神经网络用于子网络级模拟故障诊断。实例验证表明,软、硬故障统一样本集使得用于神经网络训练所需样本数目大大减少,但经过训练的神经网络可以诊断容差模拟电路的全部软、硬故障,而且诊断正确率较高。
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| 关 键 词： | 统一样本 子网络 BP神经网络 故障诊断 |
| 文章编号： | 1004-373X（2007）08-118-03 |
| 收稿时间： | 2006-08-30 |
| 修稿时间： | 2006-08-30 |
Neural Network Approach to Analog Fault Diagnosis at Sub-network Level |
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| LIU Liqiang,LI Chunming. Neural Network Approach to Analog Fault Diagnosis at Sub-network Level[J]. Modern Electronic Technique, 2007, 30(8): 118-120 |
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| Authors: | LIU Liqiang LI Chunming |
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| Affiliation: | Information Engineering School, Inner Mongolia Polytechnic University, Huhhot, 010062, China |
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| Abstract: | In this paper,the unified sample for both soft and hard faults of elements in analog circuits is found according to the invariance of node-voltage sensitivity sequence in analog circuits with tolerance.Then BP neural network is trained by use of the unified sample groups and used to analog fault diagnosis at sub-network-level.The experimental results show that samples are reduced extensively but the neural network can diagnosis both soft and hard faults of tolerance analog circuits and have a high rate of accuracy. |
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| Keywords: | unified samples sub-network BP neural network fault diagnosis |
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