Analog Circuit Fault Diagnosis Based on LVQ Neural Network

As an application of artificial intelligence technique in the field of analog circuit fault diagnosis, intelligent fault diagnosis system based on artificial neural network achieved certain success in practice. However, because neural network need for normalization preprocessing of sample before training, prolong the time of fault diagnosis, which is limited in the actual use of the diagnosis system. And the characteristics of LVQ(learning vector quantization) network is not need for normalization and other preprocessing of training samples, therefore, reducing the training time of neural networks. In this paper, the structure and training methods of the LVQ neural network are presented and the specific implementation of the diagnosis system is illustrated with examples. Simulation results show that the mathematical model has a better diagnostic effect. Compared with other methods, this diagnostic method, with the broad application prospect of its structure and method, is simple and practical and so on.