Combinatorial optimization based analog circuit fault diagnosis with back propagation neural network

Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit, a combinatorial optimization diagnosis scheme was proposed with back propagation (BP) neural network (BPNN). The main contributions of this scheme included two parts: (1) the random tolerance samples were added into the nominal training samples to establish new training samples, which were used to train the BP neural network based diagnosis model; (2) the initial weights of the BP neural network were optimized by genetic algorithm (GA) to avoid local minima, and the BP neural network was tuned with Levenberg-Marquardt algorithm (LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability, and effectively promotes analog circuit fault diagnosis performance based on BPNN.