.png){kind=icon}
返回
Attention-enhanced reservoir computing
.png){kind=icon}
OA
AI
DOI：10.1103/PhysRevApplied.22.014039.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Photonic reservoir computing has been successfully utilized in time-series prediction as the need for hardware implementations has increased. Prediction of chaotic time series remains a significant challenge, an area where the conventional reservoir computing framework encounters limitations of prediction accuracy. We introduce an attention mechanism to the reservoir computing model in the output stage. This attention layer is designed to prioritize distinct features and temporal sequences, thereby substantially enhancing the prediction accuracy. Our results show that a photonic reservoir computer enhanced with the attention mechanism exhibits improved prediction capabilities for smaller reservoirs. These advancements highlight the transformative possibilities of reservoir computing for practical applications where accurate prediction of chaotic time series is crucial.
Keyword:
CHAOS
期刊
IF:
4.4
论文数:
7.1K
被引数:
2.8W
机构
引用论文
分享收藏Parallel photonic information processing at gigabyte per second data rates using transient states.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Laser dynamical reservoir computing with consistency: an approach of a chaos mask signalOPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏Information processing using a single dynamical node as complex system使用单个动态节点作为复杂系统的信息处理NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏分享收藏Adaptive model selection in photonic reservoir computing by reinforcement learningSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏
查看更多内容