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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Meie Shen Minfang Peng Jianbiao He Kai Xie |
| Copyright Year | 2012 |
| Description | Author affiliation: School of Information Science and Technology, Central South University, Changsha, China (Minfang Peng) || Electronics and Information School, Yangtze University, Hubei, China (Kai Xie) || College of Computer Science, Beijing University of Information Science and Technology, Beijing, China (Meie Shen) || College of Electrical and Information Engineering, Hunan University, Changsha, China (Jianbiao He) |
| Abstract | An approach to fault diagnosis for analog circuits with tolerance is presented based on probabilistic neural networks. In order to overcome the difficulties in BP network based diagnosis such as slow learning speed for convergence and easily falling into local minimum value, probabilistic neural network is introduced to tolerance-circuit diagnosis. Fault samples including soft faults and hard faults in tolerance circuits are generated by Monte Carlo analysis. Fault features are extracted by using the largest deviation path so as to obtain appropriate training samples. Simulation results show that the proposed diagnosis method has high speed and accurate recognition even for soft faults in circuits with tolerance. |
| Starting Page | 13 |
| Ending Page | 16 |
| File Size | 102301 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467302418 |
| e-ISBN | 9781467302425 |
| DOI | 10.1109/ICCSE.2012.6295016 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-07-14 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer science Analog circuit Fault diagnosis Probabilistic Neural Network Tolerance Australia |
| Content Type | Text |
| Resource Type | Article |
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