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Study on long short-term memory based on vector direction of flood process for flood forecasting. [PDF]

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Xie T, Hu C, Liu C, Li W, Niu C, Li R.
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Long Short-Term Memory

Neural Computation, 1997
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM ...
Sepp Hochreiter, Jürgen Schmidhuber
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A Modified Long Short-Term Memory Cell

International Journal of Neural Systems, 2023
Machine Learning (ML), among other things, facilitates Text Classification, the task of assigning classes to textual items. Classification performance in ML has been significantly improved due to recent developments, including the rise of Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Transformer ...
Giannis, Haralabopoulos, Gerasimos, Razis, Ioannis, Anagnostopoulos   +2 more
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Long short-term memory with activation on gradient

Neural Networks, 2023
As the number of long short-term memory (LSTM) layers increases, vanishing/exploding gradient problems exacerbate and have a negative impact on the performance of the LSTM. In addition, the ill-conditioned problem occurs in the training process of LSTM and adversely affects its convergence.
Chuan Qin, Liangming Chen, Zangtai Cai, Mei Liu, Long Jin   +4 more
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Long short-term memory recurrent neural network architectures for large scale acoustic modeling

Interspeech, 2014
Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that was designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs.
Hasim Sak, A. Senior, F. Beaufays
semanticscholar   +1 more source

Immediate Memory for Faces: Long- or Short-Term Memory?

Quarterly Journal of Experimental Psychology, 1973
Immediate recognition memory span and short-term forgetting for non-verbal stimuli (“unfamiliar faces”) were investigated in normal subjects and amnesic patients. Surnames were used as a verbal control. It was found that normal subjects had a reliable immediate recognition span of one for faces and that there was no decrement in performance in the ...
Angela M. Taylor, Elizabeth K. Warrington   +1 more
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Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model

IEEE transactions on power electronics, 2021
Battery fault diagnosis is essential for ensuring safe and reliable operation of electric vehicles. In this article, a novel battery fault diagnosis method is presented by combining the long short-term memory recurrent neural network and the equivalent ...
Da Li, Zhaosheng Zhang, Peng Liu, Zhenpo Wang, Lei Zhang   +4 more
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Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network

IEEE Transactions on Industrial Informatics, 2020
Soft sensor has been extensively utilized in industrial processes for prediction of key quality variables. To build an accurate virtual sensor model, it is very significant to model the dynamic and nonlinear behaviors of process sequential data properly.
Xiaofeng Yuan, Lin Li, Yalin Wang
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Long Short-Term Memory with Smooth Adaptation [PDF]

2019 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), 2019
Long Short-Term Memory (LSTM) is a type of recurrent neural network that has become important in machine learning research thanks to its high precision to solve problems such as speech recognition, handwriting recognition, natural text compression, sequential data processing among others. Although classic LSTM are powerful tools to solve such problems,
Carlos Villaseñor, Alma Y. Alanis, Nancy Arana-Daniel, Francisco Mojica   +3 more
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Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

IEEE Transactions on Vehicular Technology, 2018
Remaining useful life (RUL) prediction of lithium-ion batteries can assess the battery reliability to determine the advent of failure and mitigate battery risk. The existing RUL prediction techniques for lithium-ion batteries are inefficient for learning
Yongzhi Zhang, R. Xiong, Hongwen He, M. Pecht   +3 more
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