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

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

Author: Grafiati

Published: 28 September 2022

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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.

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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

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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.

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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.

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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.

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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

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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.

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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 p

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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.

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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.

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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 interacti

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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.

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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

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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.

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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

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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.

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