| Autor: |
Dali Wu, Jiayang Kong, Zhicheng Li, Guojing Zhang, Huaicong Zhang, Jing Liang, Xing Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 107631-107644 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3438992 |
| Popis: |
We propose the Enhanced Predictive Recurrent Neural Network (EnPredRNN) based on PredRNN to extend the period of spatio-temporal prediction. To better capture global spatial dependencies, we integrate the Self-Attention (SA) module into PredRNN’s basic unit, forming the Enhanced Long Short-Term Memory (EnLSTM). Aiming at the problem that the standard temporal memory state in PredRNN contains insufficient information about inter-frame motion, we propose the Enhanced Temporal Memory (ETM) by aggregating past multi-step temporal memory states. Aiming at the gradient vanishing problem in Recurrent Neural Networks (RNN), the Alleviating Gradient Vanishing (AGV) structure is used to construct the high-speed path that facilitates gradient propagation. Experimental results show that EnPredRNN effectively extends spatio-temporal prediction from ten to thirty time-steps. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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