Long Short-Term Memory Network - an overview - ScienceDirect
Jul 7, 2020 · LSTM outperformed other models such as support vector regression (SVR), autoregressive integrated moving average (ARIMA), and bidirectional long, short term memory …
A survey on long short-term memory networks for time series …
Jan 1, 2021 · LSTM autoencoder The decoding and encoding of information is often realized in an autoencoder structure. In [19] a stacked LSTM autoencoder solves the problem of high …
RNN-LSTM: From applications to modeling techniques and …
Jun 1, 2024 · LSTM has been specifically designed to address the issue of vanishing gradients, which makes vanilla RNNs unsuitable for learning long-term dependencies (Jaydip and Sidra, …
Working Memory Connections for LSTM - ScienceDirect
Dec 1, 2021 · Since its introduction, LSTM has gained a lot of attention for its flexibility and efficacy in many different tasks. To simplify the LSTM structure, Liu et al. (2020) propose to …
Long Short-Term Memory - an overview | ScienceDirect Topics
Long short-term memory (LSTM) (Hochreiter and Schmidhuber, 1997) is a variant of recurrent neural networks (RNNs) that has already been discussed in Section 5 and illustrated in Fig. 8. …
PI-LSTM: Physics-informed long short-term memory
Oct 1, 2023 · Nevertheless, as the number of training samples increased from 10 to 60, both LSTM and PI-LSTM exhibited enhanced accuracy, with LSTM experiencing a particularly …
Interpretable spatio-temporal attention LSTM model for flood ...
Aug 25, 2020 · At the same time, SA-LSTM and TA-LSTM also performed better, which is better than LSTM model mostly. At t + 1 , all the models perform well, and errors increase to different …
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 input for the LSTM can be described by u LSTM, k = x 1, k … x n, k u 1, k … u m, k where x i, k is the i th measured system state at time instance k of the ODE and u i, k the …
A multi-source transfer learning model based on LSTM and …
Jul 1, 2023 · The LSTM network was proposed by Hochreiter et al. [45]. As a variant of the recurrent neural network (RNN), LSTM can capture the temporal correlation of sequences and …