@inproceedings{c8df9bf0f86a43abb87dc6c56fc663e3,
title = "A Novel Test Stimulus Generation Method Based On Ensemble Learners for Analog Circuits",
abstract = "Test stimulus generation is a very effective tool in analog circuit fault diagnosis. The extensive usage of machine learning methods in analog circuit fault diagnosis has verified their good interpretability and effectiveness. This paper proposes a test stimulus generation method for analog circuits based on integrating four machine- learning methods. It selects frequency by classifying the frequency domain signals, fusion of feature importance, and one-to-one correspondence between features and frequencies. To verify the effectiveness of the proposed method, it was validated using the Sallen-Key bandpass filter circuit and the four-op-amp biquadratic high-pass circuit. The experimental results show that the test stimulus obtained by the proposed method can effectively excite the fault features and improve the diagnosis accuracy.",
keywords = "analog circuits, fault diagnosis, machine learning, test stimulus generation",
author = "Tianyu Gao and Haochi Yang and Jiapeng Zuo and Lexiao Li and Xiaopeng Fan and Xiaodong Liu",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024 ; Conference date: 31-10-2024 Through 03-11-2024",
year = "2024",
doi = "10.1109/ICSMD64214.2024.10920505",
language = "英语",
series = "ICSMD 2024 - 5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICSMD 2024 - 5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence",
address = "美国",
}