Accurate reliability allocation of complex system using neutral networks

For realizing accurate reliability allocation of complex system, a method based on inverse thinking is proposed in this paper. The important of every sub-system is determined by analyzing the inter-relationship between the changing of sub-system reliability and system reliability. The accurate reliability allocation model of the complex system is established based on the neural networks. The self-restriction conditions of every sub-system and the system reliability are taken as the input variables of the model, the bio-mutual ratio of sub-system reliability is taken as the output variables of the model. The important of every sub-system in the prophase reliability experiment is learned by the trained neural networks. Then, a positive reciprocal matrix can be formed by the output variables of the model. The reliability allocation coefficients of every sub-system can be achieved by solving the positive reciprocal matrix, and the expected reliability of every sub-system can also be achieved by substituting the reliability allocation coefficients into the reliability allocation formula. It is proved by the examples that the result of the model is completely according with the reliability allocation rules; the artificial factors in reliability allocation are avoided.