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 Tan and Majid Forghani-elahabad

Reliability Engineering and System Safety, 2023, vol. 235, issue C

Abstract: Reliability is an important tool for evaluating the performance of modern networks. Currently, it is NP-hard and #P-hard 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.

Keywords: Long short-term memory (LSTM); Monte carlo simulation (MCS); Binary-addition-tree algorithm (BAT); Statistical characteristics; Binary-state network reliability (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:235:y:2023:i:c:s0951832022005695

DOI: 10.1016/j.ress.2022.108954

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