Abstract
This paper proposed a new fault diagnosis method based on the extension genetic algorithm (EGA) for analog circuits. Analog circuits were difference at some node with the normal and failure conditions. However, the identification of the faulted location was not easily task due to the variability of circuit components. So this paper presented a novel EGA method for fault diagnosis of analog circuits, EGA is a combination of extension theory (ET) and genetic algorithm (GA). In the past, ET had to depend on experiences to set the classical domain and weight, but setting classical domain and weight were tedious and complicated steps in classified process. In order to improve this defect, this paper proposes an EGA to find the best parameter of classical domain and increase accuracy of the classification. The proposed method has been tested on a practical analog circuit, and compared with other classified method. The application of this new method to some testing cases has given promising results.
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Wang, MH., Chao, KH., Chung, YK. (2010). Fault Diagnosis of Analog Circuits Using Extension Genetic Algorithm. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_56
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DOI: https://doi.org/10.1007/978-3-642-13495-1_56
Publisher Name: Springer, Berlin, Heidelberg
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