Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Application of Long Short-Term Memory Recurrent Neural Networks Based on
  the BAT-MCS for Binary-State Network Approximated Time-Dependent Reliability
  Problems   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.

7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ