# Application of Long Short-Term Memory Recurrent Neural Networks Based on the BAT-MCS for Binary-State Network Approximated Time-Dependent Reliability Problems

arXiv: https://arxiv.org/abs/2202.07837 · HTML: https://mlsanity.com/paper/2202.07837/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction](https://mlsanity.com/paper/2101.06850/) (2021)
- [Novel Binary-Addition Tree Algorithm (BAT) for Binary-State Network Reliability Problem](https://mlsanity.com/paper/2004.08238/) (2020)
- [Novel Algorithm for Computing All-Pairs Homogeneity-Arc Binary-State Undirected Network Reliability](https://mlsanity.com/paper/2105.01500/) (2021)
- [Novel Binary Addition Tree Algorithm (BAT) for Calculating the Direct Lower-Bound of the Highly Reliable Binary-State Network Reliability](https://mlsanity.com/paper/2110.13390/) (2021)
- [New Binary-Addition Tree Algorithm for the All-Multiterminal Binary-State Network Reliability Problem](https://mlsanity.com/paper/2111.10818/) (2021)
- [Self-Adaptive Binary-Addition-Tree Algorithm-Based Novel Monte Carlo Simulation for Binary-State Network Reliability Approximation](https://mlsanity.com/paper/2201.05764/) (2022)

## Abstract (arXiv)

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 LSTM-BAT-MCS, based on long short-term memory (LSTM), the Monte Carlo simulation (MCS), and the binary-adaption-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.