Fault diagnosis in analog circuits based on combined-optimization BP neural networks

The electronic system's reliability becomes the key of the normal operation of whole system; so circuit fault diagnosis has attracted more and more attention. The method based on BP neural networks is an effective approach of analog circuit fault diagnosis. In this paper, aiming at the drawbacks of fault diagnosis methods based on BP neural networks for analog circuit, a combinatorial optimization diagnosis scheme is proposed. First, the initial weights of BP neural networks are optimized by genetic algorithm (GA) to avoid local minima in the scheme, and then the BP neural networks is finely tuned with Levenberg-Marquardt (L-M) method in the local solution space to look for the optimum solution or approximate optimal solutions. The scheme makes good use of the mapping capabilities of BP neural networks and the global search ability of GA; it also accelerates the networks' learning speed. Experimental results show preliminarily that the scheme comprehensively improves the whole learning process approximation and generalization ability, and effectively promotes analog circuit fault diagnosis performance based on BP neural networks.