关键词: 量子计算, 量子神经网络, 发动机, 故障诊断

Abstract:

In order to solve the problem of slow convergence speed and low classification accuracy in common BP neural network(CBPN), a model of quantum BP neural network(QBPN) was proposed. Quantum computation was introduced into CBPN. The structure of QBPN contained input layer, hidden layer and output layer, where input and transfer function were represented by quantum bit, and the output was real value. Firstly, the real-valued training samples were transformed into quantum training samples, which was as the input. Then, with transfer function, the quantum weights were calculated, and the network parameters were updated to achieve the required result. Finally, the trained network  was used for fault diagnosis, and the result was output in real value. The proposed method was applied in fault diagnosis of engine. The results indicate that, compared with CBPN, QBPN has great advantages of convergence speed, classification accuracy and executing time.

Key words: quantum computation;quantum neural network;engine;fault , diagnosis