Soft Fault Diagnosis Of Tolerance Analog Circuits Based On Quantum Neural Network

Analog circuit fault diagnosis has been very necessary and meaningful, and it has become a hot topic for research. There are many ways for diagnosing traditional analog circuit fault, but they are mainly used to diagnose hardware faults such as open, short circuits, those soft faults such as similar device defects or gradually invalidation existing in various parts of the circuit are hard to be found. In addition, in many cases, there is only one testing point for some circuits, that is, its output, traditional methods are unable to effectively carry out their diagnosis.The method of analog circuit fault diagnosis based on neural network can solve these problems properly. The neural network used in analog circuit fault diagnosis are BP network, SOFM network, fuzzy neural network combined with fuzzy theory, wavelet neural network combined with wavelet analysis. But there are still problems, such as how to accurately quantify the ambiguity, how to construct features that can show types of the faults after the wavelet transform of fault signal. All of these need further study [1].Quantum Neural Network, which is simplified as QNN, has a kind of inherent fuzziness because its hidden neurons adopt multi-layer stimulating functions. It can reasonably distribute uncertainty data to every model, Thereby the uncertainty of Pattern Recognition is reduced and the accuracy of pattern recognition is improved. Quantum neural network has been successfully applied to image processing, weather forecasts and voice recognition, but using in analog circuit fault diagnosis is rare. By combining different faults extraction methods, the paper proposes soft fault diagnosis of analog circuit based on quantum neural network, applies quantum neural network successfully to the soft fault diagnosis of analog circuits through the simulation. By comparing with BP neural network, the accuracy of fault diagnosis is improved.