A quantum neural networks data fusion algorithm and its application for fault diagnosis

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. © Springer-Verlag Berlin Heidelberg 2005.