复杂系统可靠控制中的潜在问题互连神经网络分析方法

Sneak Circuit Analysis Based on Novel Coadjacent Neural Network Model for Reliability Control of Complex System

潜在问题是影响大型复杂系统安全性、可靠性的重要因素.神经网络是一种新的潜在问题分析方法,但是其分析结果难以解释.本文提出了一种基于电路结构的神经网络模型(Neural network model based on circuit architecture,CArNN),将CArNN作为个体进行集成,形成神经网络集成用于潜在问题分析.对CArNN模型的鲁棒性进行了分析,提出了两个保证模型鲁棒性的约束条件.利用此方法对一个经典电路进行了分析,结果显示,此方法对潜在电路的正确识别率达到94%.

Sneak circuits have important influence on systems＇ safety and reliability. Artificial neural network （ANN） is a new sneak circuit analysis （SCA） method. However, it is difficult to interpret why ANN gives the conclusion. A novel coadjacent neural network model based on circuit architecture, named CArNN, is proposed. Ensemble of the CArNN was employed to analysis the sneak circuits existing in systems. Two constraints guaranteeing the robustness of CArNN were given. A typical circuit containing sneak circuit was used to verify the method. The results showed 94% of the sneak circuits can be correctly recognized by the method.