Application Research On Analog Circuits Fault Diagnosis Based On Neural Network Technology

Statistics data show that most of the electronic equipment failure is caused by analog circuit fault .So analog circuit diagnosis theory is attended by many electronic experts in the word. Network technology, on the other hand, is greatly developed. So remote network testing and fault diagnosis technology become a new research projects which is attended many technical experts. This paper focus on the issue of fault diagnosis technology, the application of neural networks in analog circuit is studied positively.Many traditional analog circuit fault diagnosis method is not very efficiency. Such as Fault Dictionary, due to the impact of tolerance components, the results are far from the application in diagnosis of digital circuits; for Parameter Identification, this method need to calculate a large number of nonlinear equations, so the workload is excessive. Compared with the traditional diagnostic methods, modern intelligent diagnostic technology, for example, neural network fault diagnosis technology is very suitable for analog circuit fault diagnosis. It needs not to establish an accurate mathematical model for diagnosis object especially its high-speed self-organization and learning ability make it become an effective method of fault diagnosis and means. BP neural network technology for the diagnosis of this application is more in-depth studied in this paper. And the application process of this kind of analog fault diagnosis method is described in this paper.Although BP network diagnostic technology has its advantages side, the traditional BP Algorithm has its own inherent flaws, such as slow convergence, easily caught in the local minimum value. This paper focuses on a combination of genetic algorithms and LMBP algorithm to overcome the inherent shortcomings of traditional BP. In this algorithm, the genetic algorithm is used to search the optimal weights in the Overall weight space to overcome the fault that easily caught in the local minimum value. And then LMBP algorithm is used to do local fine tuning. Because LMBP algorithm has high speed convergence performance, this diagnostic speed of this method is enhanced greatly. If the neural network structure is almost perfect, this paper also discusses the use of "nominal value plus random tolerance" samples in network training to improve the efficiency of circuit fault diagnosis.Due to the objective needs of research subjects, this paper establishes prototype verification system in the remote background. According to the requirement of the...