Abstract
An information fusion algorithm based on the quantum neural networks is presented for fault diagnosis in an integrated circuit. By measuring the temperature and voltages of circuit components of mate changing circuit board of photovoltaic radar, the fault membership functional assignment of two sensors to circuit components is calculated, and the fusion fault membership functional assignment is obtained by using the 5-level transfer function quantum neural network (QNN). Then the fault component is precisely found according to the fusion data. Comparing the diagnosis results based on separate original data DS fusion data BP fusion data with the ones based on QNN fused data, it is shown that the quantum fusion fault diagnosis method is more accurate.
This project is supported by JiangSu Province Nature Science Foundation (NO. BK 2004021) and the Key Project of Chinese Ministry of Education.( NO.105088).
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Zhu, D., Chen, E., Yang, Y. (2005). A Quantum Neural Networks Data Fusion Algorithm and Its Application for Fault Diagnosis. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_61
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DOI: https://doi.org/10.1007/11538059_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28226-6
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