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.
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