论文标题
基于BAT-MC的二进制网络近似时间依赖性可靠性问题的长期短期记忆复发性神经网络应用
Application of Long Short-Term Memory Recurrent Neural Networks Based on the BAT-MCS for Binary-State Network Approximated Time-Dependent Reliability Problems
论文作者
论文摘要
可靠性是评估现代网络性能的重要工具。当前，当假定每个组件的可靠性固定时，计算二进制网络的确切可靠性是NP-HARD和＃P-HARD。但是，此假设是不现实的，因为每个组件的可靠性总是随时间而变化。为了满足这一实用要求，我们提出了一种基于长期短期记忆（LSTM），蒙特卡洛模拟（MCS）和二进制 - 适应 - 树算法（BAT）的新算法，称为LSTM-BAT-MCS。三个基准网络的实验结果证明了所提出的LSTM-BAT-MC的优势，最多最多为10-4均方根误差。
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.
