Relevant bibliographies by topics / Robust Long-Short Term Memory (RoLSTM)

Contents

  1. Journal articles

Academic literature on the topic 'Robust Long-Short Term Memory (RoLSTM)'

Author: Grafiati
Published: 28 September 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Robust Long-Short Term Memory (RoLSTM).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Robust Long-Short Term Memory (RoLSTM)"

1

Javid, Gelareh, Djaffar Ould Abdeslam, and Michel Basset. "Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks." Energies 14, no. 3 (2021): 758. http://dx.doi.org/10.3390/en14030758.

Full text
Abstract:
The State of Charge (SOC) estimation is a significant issue for safe performance and the lifespan of Lithium-ion (Li-ion) batteries. In this paper, a Robust Adaptive Online Long Short-Term Memory (RoLSTM) method is proposed to extract SOC estimation for Li-ion Batteries in Electric Vehicles (EVs). This real-time, as its name suggests, method is based on a Recurrent Neural Network (RNN) containing Long Short-Term Memory (LSTM) units and using the Robust and Adaptive online gradient learning method (RoAdam) for optimization. In the proposed architecture, one sequential model is defined for each of the three inputs: voltage, current, and temperature of the battery. Therefore, the three networks work in parallel. With this approach, the number of LSTM units are reduced. Using this suggested method, one is not dependent on precise battery models and can avoid complicated mathematical methods. In addition, unlike the traditional recursive neural network where content is re-written at any time, the LSTM network can decide on preserving the current memory through the proposed gateways. In that case, it can easily transfer this information over long paths to receive and maintain long-term dependencies. Using real databases, the experiment results illustrate the better performance of RoLSTM applied to SOC estimation of Li-Ion batteries in comparison with a neural network modeling and unscented Kalman filter method that have been used thus far.
APA, Harvard, Vancouver, ISO, and other styles
2

Fister, Dušan, Matjaž Perc, and Timotej Jagrič. "Two robust long short-term memory frameworks for trading stocks." Applied Intelligence 51, no. 10 (2021): 7177–95. http://dx.doi.org/10.1007/s10489-021-02249-x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Liu, Yong, Xin Hao, Biling Zhang, and Yuyan Zhang. "Simplified long short-term memory model for robust and fast prediction." Pattern Recognition Letters 136 (August 2020): 81–86. http://dx.doi.org/10.1016/j.patrec.2020.05.033.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Yang, Haimin, Zhisong Pan, and Qing Tao. "Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory." Computational Intelligence and Neuroscience 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9478952.

Full text
Abstract:
Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.
APA, Harvard, Vancouver, ISO, and other styles
5

Son, Namrye, Seunghak Yang, and Jeongseung Na. "Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory." Energies 12, no. 20 (2019): 3901. http://dx.doi.org/10.3390/en12203901.

Full text
Abstract:
Renewable energy has recently gained considerable attention. In particular, the interest in wind energy is rapidly growing globally. However, the characteristics of instability and volatility in wind energy systems also affect power systems significantly. To address these issues, many studies have been carried out to predict wind speed and power. Methods of predicting wind energy are divided into four categories: physical methods, statistical methods, artificial intelligence methods, and hybrid methods. In this study, we proposed a hybrid model using modified LSTM (Long short-term Memory) to predict short-term wind power. The data adopted by modified LSTM use the current observation data (wind power, wind direction, and wind speed) rather than previous data, which are prediction factors of wind power. The performance of modified LSTM was compared among four multivariate models, which are derived from combining the current observation data. Among multivariable models, the proposed hybrid method showed good performance in the initial stage with Model 1 (wind power) and excellent performance in the middle to late stages with Model 3 (wind power, wind speed) in the estimation of short-term wind power. The experiment results showed that the proposed model is more robust and accurate in forecasting short-term wind power than the other models.
APA, Harvard, Vancouver, ISO, and other styles
6

Ngoc-Lan Huynh, Anh, Ravinesh C. Deo, Mumtaz Ali, Shahab Abdulla, and Nawin Raj. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition." Applied Energy 298 (September 2021): 117193. http://dx.doi.org/10.1016/j.apenergy.2021.117193.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Darling, Stephen, Richard J. Allen, and Jelena Havelka. "Visuospatial Bootstrapping." Current Directions in Psychological Science 26, no. 1 (2017): 3–9. http://dx.doi.org/10.1177/0963721416665342.

Full text
Abstract:
Visuospatial bootstrapping is the name given to a phenomenon whereby performance on visually presented verbal serial-recall tasks is better when stimuli are presented in a spatial array rather than a single location. However, the display used has to be a familiar one. This phenomenon implies communication between cognitive systems involved in storing short-term memory for verbal and visual information, alongside connections to and from knowledge held in long-term memory. Bootstrapping is a robust, replicable phenomenon that should be incorporated in theories of working memory and its interaction with long-term memory. This article provides an overview of bootstrapping, contextualizes it within research on links between long-term knowledge and short-term memory, and addresses how it can help inform current working memory theory.
APA, Harvard, Vancouver, ISO, and other styles
8

Avci, Gunes, Steven P. Woods, Marizela Verduzco, et al. "Effect of Retrieval Practice on Short-Term and Long-Term Retention in HIV+ Individuals." Journal of the International Neuropsychological Society 23, no. 3 (2017): 214–22. http://dx.doi.org/10.1017/s1355617716001089.

Full text
Abstract:
AbstractObjectives: Episodic memory deficits are both common and impactful among persons infected with HIV; however, we know little about how to improve such deficits in the laboratory or in real life. Retrieval practice, by which retrieval of newly learned material improves subsequent recall more than simple restudy, is a robust memory boosting strategy that is effective in both healthy and clinical populations. In this study, we investigated the benefits of retrieval practice in 52 people living with HIV and 21 seronegatives. Methods: In a within-subjects design, all participants studied 48 verbal paired associates in 3 learning conditions: Massed-Restudy, Spaced-Restudy, and Spaced-Testing. Retention of verbal paired associates was assessed after short- (30 min) and long- (30 days) delay intervals. Results: After a short delay, both HIV+ persons and seronegatives benefited from retrieval practice more so than massed and spaced restudy. The same pattern of results was observed specifically for HIV+ persons with clinical levels of memory impairment. The long-term retention interval data evidenced a floor effect that precluded further analysis. Conclusions: This study provides evidence that retrieval practice improves verbal episodic memory more than some other mnemonic strategies among HIV+ persons. (JINS, 2017, 23, 214–222)
APA, Harvard, Vancouver, ISO, and other styles
9

Bukhari, Syed Basit Ali, Khawaja Khalid Mehmood, Abdul Wadood, and Herie Park. "Intelligent Islanding Detection of Microgrids Using Long Short-Term Memory Networks." Energies 14, no. 18 (2021): 5762. http://dx.doi.org/10.3390/en14185762.

Full text
Abstract:
This paper presents a new intelligent islanding detection scheme (IIDS) based on empirical wavelet transform (EWT) and long short-term memory (LSTM) network to identify islanding events in microgrids. The concept of EWT is extended to extract features from three-phase signals. First, the three-phase voltage signals sampled at the terminal of targeted distributed energy resource (DER) or point of common coupling (PCC) are decomposed into empirical modes/frequency subbands using EWT. Then, instantaneous amplitudes and instantaneous frequencies of the three-phases at different frequency subbands are combined, and various statistical features are calculated. Finally, the EWT-based features along with the three-phase voltage signals are input to the LSTM network to differentiate between non-islanding and islanding events. To assess the efficacy of the proposed IIDS, extensive simulations are performed on an IEC microgrid and an IEEE 34-node system. The simulation results verify the effectiveness of the proposed IIDS in terms of non-detection zone (NDZ), computational time, detection accuracy, and robustness against noisy measurement. Furthermore, comparisons with existing intelligent methods and different LSTM architectures demonstrate that the proposed IIDS offers higher reliability by significantly reducing the NDZ and stands robust against measurements uncertainty.
APA, Harvard, Vancouver, ISO, and other styles
10

Baddar, Wissam J., and Yong Man Ro. "Encoding features robust to unseen modes of variation with attentive long short-term memory." Pattern Recognition 100 (April 2020): 107159. http://dx.doi.org/10.1016/j.patcog.2019.107159.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources
You might also be interested in the extended bibliographies on the topic 'Robust Long-Short Term Memory (RoLSTM)' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography