Analog circuits fault diagnosis by GA-RBF neural network and virtual instruments

Analog circuits fault diagnosis is essentially a course of pattern classification. ANN (Artificial Neural Network) has the capability of approaching nonlinear function with any accuracy and can accomplish data´s processing and assorting, which makes it an ideal tool for analog circuits diagnosis and other pattern classification problems. In this paper, Genetic Algorithm is used to optimize RBF neural network, which can combine both GA´s and ANN´s benefits and avoid some specific problems, such as traditional BP neural network´s low converge and generating local minimum. On the basis of LabVIEW, the API for analog circuits fault diagnosis is established, simulation experiments are carried out to verify the effectiveness of the method of combing GA-RBF neural network with LabVIEW. Through diagnosis results, it can be seen that the method combining GA-RBF with LabVIEW can not only show the fault diagnosis results visually and directly, but also ensure a considerable diagnosis accuracy.