【作者】 易健雄； 朱大奇； 陈昕静；

【Author】 Jianxiong Yi Daqi Zhu Xinjing Chen (Research Centre of Control Science and Engineering, Southern Yangtze University, Wuxi, 214122)

【机构】 江南大学控制科学与工程研究中心；

【摘要】 本文介绍了基于多层激励函数的量子神经网络模型及其算法,并将量子神经网络模型应用于电机转子故障诊断中, 比较了BP神经网络和量子神经网络(QNN)的故障识别精度。仿真实验结果表明,量子神经网络提高了电机转子故障诊断的识别精度和容错能力。更多还原

【Abstract】 This paper introduces the Quantum Neural Networks model based on multilevel activation function and its algorithm briefly. On this basis, an application of Quantum Neural Networks for fault diagnosis of the motors rotors is presented. By comparing the diagnosis results based on BP Neural Networks and Quantum Neural Networks respectively, it is shown that the latter is more accurate than the former in the fault recognition.更多还原