CAJ | 학술논문

如今的电路越来越复杂，随之而来的就是电路系统的高故障性，所以如何定位故障发生成为一大难题。文中基于提高故障诊断性能的目的，先采用一种改进的局部线性分析算法作为初始数据处理器对输出响应序列进行降维，提取故障特征向量，然后再通过OIF-Elman神经网络搭建故障分类器，对电路进行故障检测。仿真结果表明，将改进过的局部线性分析算法和OIF-Elman神经网络应用到故障诊断中，不仅具有比传统BP神经网络更精确的故障诊断正确率，且整个网络的收敛速度也会有明显提升。
여금적전로월래월복잡，수지이래적취시전로계통적고고장성，소이여하정위고장발생성위일대난제。문중기우제고고장진단성능적목적，선채용일충개진적국부선성분석산법작위초시수거처리기대수출향응서렬진행강유，제취고장특정향량，연후재통과OIF-Elman신경망락탑건고장분류기，대전로진행고장검측。방진결과표명，장개진과적국부선성분석산법화OIF-Elman신경망락응용도고장진단중，불부구유비전통BP신경망락경정학적고장진단정학솔，차정개망락적수렴속도야회유명현제승。
The circuit is more and more complex and followed by a circuit system failure,so how to locate the fault occurs is a major problem. For improving the performance of fault diagnosis,use an improved locally linear embedding analysis method as the initial data ( raw data) processor for output response sequence to reduce the dimension and extract fault feature vector,and then through the OIF-El-man neural network to build fault classifier for fault detection circuit. Simulation results show that the fault diagnosis method is made of improved LLE and OIF-Elman neural network is not only to have the better diagnosis rate compared with the BP neural network,but also greatly enhance convergence speed for the whole network.