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Application of LSTM based on the BAT-MCS for binary-state network approximated time-dependent reliability problems
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Application of LSTM based on the BAT-MCS for binary-state network approximated time-dependent reliability problems

Wei-Chang Yeh, Chia-Ming Du, Shi-Yi TanMajid Forghani-elahabad
Reliability Engineering and System Safety, 108954
2022

摘要

Binary-addition-tree algorithm (BAT) Binary-state network reliability Long short-term memory (LSTM) Monte carlo simulation (MCS) Statistical characteristics Safety Risk Reliability and Quality Industrial and Manufacturing Engineering
Reliability is an important tool for evaluating the performance of modern networks. Currently, it is NP-hard and null to calculate the exact reliability of a binary-state network when the reliability of each component is assumed to be fixed. However, this assumption is unrealistic because the reliability of each component always varies with time. To meet this practical requirement, we propose a new algorithm called the binary-addition-tree algorithm and Monte Carlo simulation based Long Short-Term Memory (LSTM-BAT-MCS), based on long short-term memory (LSTM), the Monte Carlo simulation (MCS), and the binary-addition-tree algorithm (BAT). The superiority of the proposed LSTM-BAT-MCS was demonstrated by experimental results of three benchmark networks with at most 10 −4 mean square error.

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