TY - GEN
T1 - A method for analog circuits fault diagnosis by neural network and virtual instruments
AU - Li, Xiang
AU - Zhang, Yang
AU - Wang, Shujuan
AU - Zhai, Guofu
PY - 2011
Y1 - 2011
N2 - The values of analog circuits' input and output signals and the component parameters are continuous, and meanwhile there are inevitable tolerance and non-linear components in analog circuits, therefore the presence of these factors increases complexity of the analog circuits fault diagnosis. RBF and BP neural network are two widely used feedforward neural networks, LabVIEW is a graphical programming language, which can provide users with a visual and convenient design environment. On the basis of RBF and BP neural network, the theory and method of analog circuits fault diagnosis based on ANN (Artificial Neural Network) are described. By the way of hybrid programming with LabVIEW and Matlab, the API for analog circuits fault diagnosis is established. Experimental simulation study is carried out, respectively, using RBF and BP neural network. From the fault diagnosis results, it can be seen that the method of combining ANN with LabVIEW can not only show the fault diagnosis results visually and directly, but also ensure a considerable diagnosis accuracy.
AB - The values of analog circuits' input and output signals and the component parameters are continuous, and meanwhile there are inevitable tolerance and non-linear components in analog circuits, therefore the presence of these factors increases complexity of the analog circuits fault diagnosis. RBF and BP neural network are two widely used feedforward neural networks, LabVIEW is a graphical programming language, which can provide users with a visual and convenient design environment. On the basis of RBF and BP neural network, the theory and method of analog circuits fault diagnosis based on ANN (Artificial Neural Network) are described. By the way of hybrid programming with LabVIEW and Matlab, the API for analog circuits fault diagnosis is established. Experimental simulation study is carried out, respectively, using RBF and BP neural network. From the fault diagnosis results, it can be seen that the method of combining ANN with LabVIEW can not only show the fault diagnosis results visually and directly, but also ensure a considerable diagnosis accuracy.
KW - BP neural network
KW - LabVIEW
KW - RBF neural network
KW - analog circuits fault diagnosis
UR - https://www.scopus.com/pages/publications/79960155723
U2 - 10.1109/ISA.2011.5873270
DO - 10.1109/ISA.2011.5873270
M3 - 会议稿件
AN - SCOPUS:79960155723
SN - 9781424498574
T3 - 2011 3rd International Workshop on Intelligent Systems and Applications, ISA 2011 - Proceedings
BT - 2011 3rd International Workshop on Intelligent Systems and Applications, ISA 2011 - Proceedings
T2 - 2011 3rd International Workshop on Intelligent Systems and Applications, ISA 2011
Y2 - 28 May 2011 through 29 May 2011
ER -