@ARTICLE{Wang_Hui_Fault_2020,
 author={Wang, Hui},
 volume={vol. 69},
 number={No 1},
 journal={Archives of Electrical Engineering},
 pages={175-185},
 howpublished={online},
 year={2020},
 publisher={Polish Academy of Sciences},
 abstract={Analog circuits need more effective fault diagnosis methods. In this study, the fault diagnosis method of analog circuits was studied. The fault feature vectors were extracted by a wavelet transform and then classified by a generalized regression neural network (GRNN). In order to improve the classification performance, a wolf pack algorithm (WPA) was used to optimize the GRNN, and a WPA-GRNN diagnosis algorithm was obtained. Then a simulation experiment was carried out taking a Sallen–Key bandpass filter as an example. It was found from the experimental results that the WPA could achieve the preset accuracy in the eighth iteration and had a good optimization effect. In the comparison between the GRNN, genetic algorithm (GA)-GRNN and WPA-GRNN, the WPA-GRNN had the highest diagnostic accuracy, and moreover it had high accuracy in diagnosing a single fault than multiple faults, short training time, smaller error, and an average accuracy rate of 91%. The experimental results prove the effectiveness of the WPA-GRNN in fault diagnosis of analog circuits, which can make some contributions to the further development of the fault diagnosis of analog circuits.},
 type={Article},
 title={Fault diagnosis of analog circuit based on wavelet transform and neural network},
 URL={http://www.czasopisma.pan.pl/Content/115096/PDF/12_AEE_1_2020.pdf},
 doi={10.24425/aee.2020.131766},
 keywords={analog circuit, fault diagnosis, neural network, wavelet transform},
}