Estimating All-Terminal Network Reliability Using a Neural Network
نویسنده
چکیده
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. Because of the impracticality of calculating allterminal network reliability for networks of moderate to large size, Monte Carlo simulation methods to estimate network reliability and upper and lower bounds to bound reliability have been used as alternatives. This paper puts forth another alternative to the estimation of all-terminal network reliability – that of 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.
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