A survey on long short-term memory networks for time series …
Jan 1, 2021 · Recurrent neural networks and exceedingly Long short-term memory (LSTM) have been investigated intensively in recent years due to their ability to model and predict nonlinear …
Working Memory Connections for LSTM - ScienceDirect
Dec 1, 2021 · In our experiments, we show that an LSTM equipped with Working Memory Connections achieves better results than comparable architectures, thus reflecting the …
RNN-LSTM: From applications to modeling techniques and …
Jun 1, 2024 · Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN) algorithm known for its ability to effectively analyze and process sequentia…
PI-LSTM: Physics-informed long short-term memory
Oct 1, 2023 · The PI-LSTM network, inspired by and compared with existing physics-informed deep learning models (PhyCNN and PhyLSTM), was validated using the numerical simulation …
Fundamentals of Recurrent Neural Network (RNN) and Long Short …
Mar 1, 2020 · Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blo…
Model Predictive Control when utilizing LSTM as dynamic models
Aug 1, 2023 · The prediction model is the most important part of an MPC strategy. The accuracy of such a model influences the quality of predictions and control per…
Interpretable spatio-temporal attention LSTM model for flood ...
Aug 25, 2020 · LSTM-based methods Long Short Term Memory (LSTM) is a modified version of recurrent neural networks, which is proposed to solve the problem of long-distance (time) …
Traffic flow prediction using LSTM with feature enhancement
Mar 7, 2019 · Long short-term memory (LSTM) is widely used to process and predict events with time series, but it is difficult to solve exceedingly long-term depend…
A multi-source transfer learning model based on LSTM and …
Jul 1, 2023 · The model integrated multiple subnetworks, and LSTM extracts the temporal features of each pair of source–target buildings separately. The distribution distance between …
GT-LSTM: A spatio-temporal ensemble network for traffic flow …
Mar 1, 2024 · In contrast, GT-LSTM achieves relatively stable forecasting performance on different datasets, which indicates that GT-LSTM holds a reliable generalization ability and can …