Simulation of Open Quantum Dynamics with Bootstrap-Based Long Short-Term Memory Recurrent Neural Network

2021 ◽  
pp. 10225-10234
Author(s):  
Kunni Lin ◽  
Jiawei Peng ◽  
Feng Long Gu ◽  
Zhenggang Lan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Quantum Dynamics ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Open Quantum ◽  
Long Short Term Memory

A new methodology for kick detection during petroleum drilling using long short-term memory recurrent neural network

2020 ◽  
Vol 142 ◽  
pp. 126-137 ◽  
Author(s):  
Augustine Osarogiagbon ◽  
Somadina Muojeke ◽  
Ramachandran Venkatesan ◽  
Faisal Khan ◽  
Paul Gillard
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Petroleum Drilling

scholarly journals The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yinping Gao ◽  
Daofang Chang ◽  
Ting Fang ◽  
Yiqun Fan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Arima Model ◽  
Training Set ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Box Plot

The effective forecast of container volumes can provide decision support for port scheduling and operating. In this work, by deep learning the historical dataset, the long short-term memory (LSTM) recurrent neural network (RNN) is used to predict daily volumes of containers which will enter the storage yard. The raw dataset of daily container volumes in a certain port is chosen as the training set and preprocessed with box plot. Then the LSTM model is established with Python and Tensorflow framework. The comparison between LSTM and other prediction methods like ARIMA model and BP neural network is also provided in this study, and the prediction gap of LSTM is lower than other methods. It is promising that the proposed LSTM is helpful to predict the daily volumes of containers.

scholarly journals Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

2018 ◽  
Vol 7 (4.15) ◽  
pp. 25 ◽  
Author(s):  
Said Jadid Abdulkadir ◽  
Hitham Alhussian ◽  
Muhammad Nazmi ◽  
Asim A Elsheikh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting models.  

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