Provided is an analog circuit fault diagnosis method based on vector-valued regularized kernel function approximation. The method comprises the following steps: (1) acquiring a fault response voltage signal of an analog circuit; (2) carrying out wavelet packet transformation on the collected signal, and calculating a wavelet packet coefficient energy value as a characteristic parameter; (3) utilizing a quantum particle swarm optimization algorithm to optimize a regularization parameter and kernel parameter of vector-valued regularized kernel function approximation, and training a fault diagnosis model; and (4) utilizing the trained diagnosis model to recognize circuit faults. The analog circuit diagnosis method based on vector-valued regularized kernel function approximation has a better classification performance than the other classification algorithms, and the method using a quantum particle swarm optimization algorithm to optimize parameters is also better than traditional methods for acquiring parameters. The diagnosis method can efficiently diagnose faults of elements of a circuit.
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