# Ensemble long short-term memory (EnLSTM) network

arXiv: https://arxiv.org/abs/2004.13562 · HTML: https://mlsanity.com/paper/2004.13562/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning](https://mlsanity.com/paper/2210.02448/) (2022)
- [Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development](https://mlsanity.com/paper/2206.09606/) (2022)
- [A Noise-robust Multi-head Attention Mechanism for Formation Resistivity Prediction: Frequency Aware LSTM](https://mlsanity.com/paper/2406.03849/) (2024)

## Abstract (arXiv)

In this study, we propose an ensemble long short-term memory (EnLSTM) network, which can be trained on a small dataset and process sequential data. The EnLSTM is built by combining the ensemble neural network (ENN) and the cascaded long short-term memory (C-LSTM) network to leverage their complementary strengths. In order to resolve the issues of over-convergence and disturbance compensation associated with training failure owing to the nature of small-data problems, model parameter perturbation and high-fidelity observation perturbation methods are introduced. The EnLSTM is compared with commonly-used models on a published dataset, and proven to be the state-of-the-art model in generating well logs with a mean-square-error (MSE) reduction of 34%. In the case study, 12 well logs that cannot be measured while drilling are generated based on logging-while-drilling (LWD) data. The EnLSTM is capable to reduce cost and save time in practice.