Optimization of LM-BP neural network algorithm for analog circuit fault diagnosis

There are inherent disadvantages in traditional BP neural network. First, the error of training drops slowly. Second, the adjustment time is too long because of too many iteration steps. Last but not least it even easily falls into local minimum and is hardly able to extricate itself, which leads to low accuracy of diagnosis. Therefore, a new BP network method optimized by genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm is proposed. In this method, the BP network´s structure is optimized by GA. Then LM algorithm is used to train the BP network. The training result could diagnose the faults of analog circuits, which is able to overcome the inherent disadvantages of traditional BP network. Several simulation and experimental results are presented, demonstrating the effectiveness and applicability of the developed method.