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VECTOR-VALUED REGULARIZED KERNEL FUNCTION APPROXIMATION BASED FAULT DIAGNOSIS METHOD FOR ANALOG CIRCUIT
VECTOR-VALUED REGULARIZED KERNEL FUNCTION APPROXIMATION BASED FAULT DIAGNOSIS METHOD FOR ANALOG CIRCUIT
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机译:矢量值正则化核函数逼近基于模拟电路的故障诊断方法
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摘要
A vector-valued regularized kernel function approximation (VVRKFA) based fault diagnosis method for an analog circuit comprises the following steps: (1) obtaining fault response voltage signals of an analog circuit; (2) performing wavelet packet transform on the collected signals, and calculating wavelet packet coefficient energy values as feature parameters; (3) optimizing regularization parameters and kernel parameters of VVRKFA by using a quantum particle swarm optimization algorithm and training a fault diagnosis model; and (4) identifying a circuit fault by using the trained diagnosis model. In the invention, the classification performance of the VVRKFA method is superior to other classification algorithms, and optimization of parameters by the quantum particle swarm optimization (QPSO) algorithm is also superior to the traditional method of obtaining parameters. The fault diagnosis method provided by the invention can efficiently diagnose the component faults of the circuit, including soft faults and hard faults.
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