Optimization of the neural RBF classifier for the diagnostics of electronic circuit |
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| Bartosz Polok, Piotr Bilski |
- Abstract:
- The paper presents the detailed analysis of the Radial Basis Function (RBF) neural network application for diagnostics of analog systems. In most cases RBF networks are used in the approximation tasks. In this work the network is used as the fault classifier. Because RBF networks are known to be dependent on the size of training data, the procedure to minimize the amount of the learning examples was proposed. The classifier is tested on the model of the electronic filter, which, was also diagnosed by alternative methods, such as Multilayered Perceptron and Support Vector Machine. Experimental results show what are advantages and drawbacks of the RBF classifier compared to other solutions.
- Download:
- IMEKO-TC10-2017-020.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