摘要
对一类较典型的模拟电路进行了神经网络的研究和建模,分别建立了基于反向传播BP网络、径向基函数RBF网络和支持向量机SVM的故障诊断模型,分析和比较了3类模型在电路故障中的不同性能,并提出不同模型在诊断过程中对应不同故障诊断策略的观点.结果表明:SVM模型的诊断精度较高,在处理不确定信号时SVM和RBF模型表现为"视低"策略,而BP模型表现为"视高"策略,这为实际电路故障诊断模型的选择提供了一定的研究依据.
A kind of analog circuit was studied and modeled for fault diagnosis based on the neural networks,including the backward propagation（BP） neural network,the radial basis function（RBF） network and the support vector machine（SVM）.We established three diagnosis models mentioned above,compared their diagnosis performances,and advanced a viewpoint that a different model corresponds to a different strategy during faults diagnosis.The simulating results show that,the SVM model has higher diagnosis precision and indicates a ＂high regarded as low＂ strategy with the RBF model,while the BP model presents as a ＂low regarded as high＂ strategy when dealing with the uncertain signals,which provides a basis for further theoretical study on the fault diagnosis of the analog circuits.
出处
《中北大学学报（自然科学版）》
CAS
北大核心
2010年第3期232-237,共6页
Journal of North University of China(Natural Science Edition)
关键词
模拟电路
故障诊断
反向传播网络
径向基函数网络
支持向量机
analog circuits
fault diagnosis
BP neural network
radial basis function network
support vector machine
