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- W2353481261 abstract "In order to diagnosis the analog circuits of single fault and multiple faults more efficiently and accurately,an approach based on lifting wavelet(LW) and RBF neural networks is proposed.The impulse response signal of circuit under test(CUT) is sampled and decomposed to form fault features,training RBF neural networks as classifier to diagnose the CUT.The filter experiment has proved that presented approach can diagnose single fault and multiple faults of CUT more efficiently. Compared with the traditional wavelet diagnosis rate of 96.8%,diagnosis rate of lifting wavelet can reach 99.2% and with less time.The results showed that analog circuit single and multiple faults diagnosis using lifting wavelet and RBF neural networks can effectively extract the fault features and carry on the diagnosis for the fault accurately and rapidly." @default.
- W2353481261 created "2016-06-24" @default.
- W2353481261 creator A5000478653 @default.
- W2353481261 date "2015-01-01" @default.
- W2353481261 modified "2023-09-26" @default.
- W2353481261 title "Analog Circuit Single and Multiple Faults Diagnosis Based on Lifting Wavelet and RBF Neural Networks" @default.
- W2353481261 hasPublicationYear "2015" @default.
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