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 time-variant system dynamics. The present paper delivers a comprehensive overview of existing LSTM cell derivatives and network architectures for time series prediction.

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…

Jul 7, 2020 · A Long Short-Term Memory Network, also known as LSTM, is an advanced recurrent neural network that uses "gates" to capture both long-term and short-term memory. These gates help prevent the issues of gradient exploding and vanishing that occur in standard RNNs. LSTM has a well-constructed structure with gates named as "forget gate," "input gate," …

Dec 1, 2021 · For the sMNIST task, peephole LSTM performs slightly better than vanilla LSTM. LSTM with Working Memory Connections, instead, outperforms the competing architectures in terms of final accuracy and convergence speed.

LSTM addresses the vanishing gradient problem by providing longer-lived short-term memory to preserve information across timesteps. Generally, an LSTM node contains a memory cell, an input gate, an output gate, and a forget gate. The memory cell serves as an information store, while the gates regulate the flow of that information (Du et al., 2016).

Sep 1, 2020 · This paper introduces an innovative physics-informed deep learning framework for metamodeling of nonlinear structural systems with scarce data. The ba…

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 results of the single-degree-of-freedom (SDOF) system and …

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…

Mar 1, 2020 · All major open source machine learning frameworks offer efficient, production-ready implementations of a number of RNN and LSTM network architectures. Naturally, some practitioners, even if new to the RNN/LSTM systems, take advantage of this access and cost-effectiveness and proceed straight to development and experimentation.

Feb 1, 2022 · In this approach, LSTM was employed to predict the residual errors of WRF-Hydro; in contrast, the conventional approach with LSTM predicts streamflow directly. Here, we performed numerical experiments to predict the inflow of Soyangho Lake in South Korea using WRF-Hydro-LSTM, WRF-Hydro-only, and LSTM-only.