@inproceedings{be0b9e8551b34a96affc10a90222da2f,
title = "Research on Conducted Immunity Quantification Model of Operational Amplifier Based on Neural Networks and Feature Extraction",
abstract = "The quantitative model presented in this study focuses on analyzing and predicting the output behavior of conducted immunity operational amplifiers. By testing conducted immunity on operational amplifiers, a conducted immunity model is established using the time-domain waveform feature extraction and neural network method. The results demonstrate that the quantized model exhibits an error accuracy within 2 dB when compared to test data. Furthermore, the quantized model accurately predicts time-domain waveform features with an error accuracy within 1.2 dB.",
keywords = "ICIM-CI, immunity, neural networks, operational amplifiers, vector fitting",
author = "Mengyuan Wei and Shuguo Xie and Xi Chen and Peng Huang and Xuchun Hao and Xiaozong Huang and Shuling Zhou and Xiaokang Wen",
note = "Publisher Copyright: {\textcopyright} 2023 Applied Computational Electromagnetics Society (ACES).; 2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023 ; Conference date: 15-08-2023 Through 18-08-2023",
year = "2023",
doi = "10.23919/ACES-China60289.2023.10249590",
language = "英语",
series = "2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023",
address = "美国",
}