Estimating all-terminal network reliability using a neural network

The exact calculation of all-terminal network reliability is an NP-hard problem, with computational effort growing exponentially with the number of nodes and links in the network. Due to the impracticality of calculating all-terminal network reliability for networks of moderate to large size, Monte Carlo simulation methods have been used to estimate the network reliability and reliability upper and lower bounds. This paper puts forth an alternative to the estimation of all-terminal network reliability by using artificial neural network predictive models. Neural networks are constructed, trained and validated using alternative network topologies, a network reliability upper bound and the exact network reliability as a target. A hierarchical approach is used: a general neural network screens all network designs for reliability followed by a specialized neural network for highly reliable network designs. Results on a ten node problem are given using a grouped cross validation approach.