Unsupervised learning-based hierarchical diagnosis of analog circuits |
|---|
| Piotr Bilski |
- Abstract:
- The paper presents the automated diagnostic method of analog circuits combining supervised and unsupervised learning. The purpose of this sophisticated approach is to effectively distinguish easily identified states of the analyzed system from the more difficult ones. The latter are often related with existence of ambiguity groups, which create problems for the distinction of specific states. The proposed approach uses subsequently two algorithms. The first one extracts and learns to classify such “simple” training examples. The second one aims at the classification of more difficult ones. For both stages, Self-Organizing Maps and Random Forest were used, respectively. The scheme was tested on the model of the 3rd order Bessel highpass filter, confirming effectiveness of the approach.
- Download:
- IMEKO-TC10-2017-016.pdf
- DOI:
- -
- Event details
- IMEKO TC:
- TC10
- Event name:
- TC10 Workshop on Technical Diagnostics 2017
- Title:
15th IMEKO TC10 Workshop "Technical Diagnostics in Cyber-Physical Era"
- Place:
- Budapest, HUNGARY
- Time:
- 06 June 2017 - 07 June 2017