主成分分析法与概率神经网络在模拟电路故障诊断中的应用

模拟电路故障的多样性使得神经网络训练样本数量增加,BP网络结构趋于复杂,训练速度降低;针对反向传播神经网络(BPNN)学习收敛速度慢、易陷入局部极小值等问题,提出了基于主成分分析(PCA)与概率神经网络(PNN)相结合的模拟电路故障诊断方法;通过主成分分析法(Principal Component Analysis)提取特征数据进行降维处理,再结合概率神经网络(Probabilistic Neural Networks)对电路故障进行分类;实例说明采用PCA和PNN结合对故障数据处理,可以大大的提高故障诊断分类的准确性。

Principal Component Analysis and Probabilistic Neural Networks and Its Application in Fault Diagnosis of Analog Circuits

The variety of faults in analog circuit makes the number of training samples of neural network greatly increase.Structure of BP network tends to be complex and training rate is greatly reduced.Against the shortcomings of Back-propagation Neural Network (BPNN),which include slow learning speed of convergence and the nature which is easy to fall into local minimum value,Probabilistie Neu- ral Networks(PNN)conbined with Principal Component Analysis(PCA)based diagnostic method for faults of analog circuit with tolerance is proposed.Through PCA lowerring dimension by data characteristic distill,PNN identify and classify faults.An example indicates the pre- cision of fault diagnostic by the processed data of PCA and PNN.