基于小波与量子神经网络的容差模拟电路的软故障诊断

journal6 ›› 2008, Vol. 29 ›› Issue (4): 68-70.

基于小波与量子神经网络的容差模拟电路的软故障诊断

（1．湖南大学电气与信息工程学院，湖南　长沙 4１００８３；２．吉首大学物理科学与信息工程学院，湖南　吉首 4１６０００）

出版日期:2008-07-25 发布日期:2012-05-21
作者简介:李云红(1975-)，女(土家族)，湖南桑植人，吉首大学物理与信息工程学院讲师，湖南大学电气与信息工程学院硕士生，主要从事模拟电路故障诊断研究.
基金资助:
国家自然科学基金资助项目(50277010)；湖南省自然科学基金资助项目(07JJ6132)

Soft Fault Diagnosis of Tolevance Simulated Circuit Based on Wavelet and Quantum Neural Network

(1.College of Electric and Information Engineering,Hunan University,Changsha 410083,China;2.College of Physics Science and Information Engineering,Jishou University,Jishou 416000,Hunan China)

Online:2008-07-25 Published:2012-05-21

摘要/Abstract

摘要：量子神经网络（Quantum Neural Network，简称QNN)的隐层神经元采用多层激励函数，具有一种固有的模糊性,能将决策的不确定性数据合理地分配到各模式中,从而减少模式识别的不确定度,提高模式识别的准确性.笔者提出了基于小波与量子神经网络的容差模拟电路的软故障诊断，实验仿真分析表明：该诊断方法正确率可提高2478%,从而提高了故障诊断的正确性.

关键词： 量子神经网络, 量子间隔, 小波分析, 容差分析

Abstract: The hidden neurous in quantum neural network adopt the multi-level actioation function;so,with fixed fuzziness,the quantum network can reasonably distribute the uncertain data of a decision into each mode,thus to reduce the uncertainty of mode recognition and raise the accuracy of the mode recognition.The soft fault diagnosis of tolerance simulated circuit based on wavelet and quantum neural network is proposed.The simulation experiment shows that,compared with BP neural network,the quantum neural network can increase the fault diagnosis accuracy to 24.78%.

Key words: quantum neural network( QNN), quantum interval, wavelet analysis, tolerance analysis

李云红, 谭阳红, 龙波华. 基于小波与量子神经网络的容差模拟电路的软故障诊断[J]. journal6, 2008, 29(4): 68-70.

LI Yun-Hong, TAN Yang-Hong, LONG Bo-Hua. Soft Fault Diagnosis of Tolevance Simulated Circuit Based on Wavelet and Quantum Neural Network[J]. journal6, 2008, 29(4): 68-70.