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Dendritic learning-based gravitational waves prediction model

delete2026-02-01
delete0
PRE
AI
D
Dongbao Jia
W
W. Xu
Z
Zhaoman Zhong
J
Jing Sun
S
Shigeki Hirobayashi
Z
Zhenyu Lei
S
Shangce Gao *
DOI：10.1016/j.ins.2026.123218delete
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摘要

摘要

En 中文
引力波（GW）去噪是引力波分析中的一个热门话题，时频分析是GW去噪的主流信号处理方法。由于噪声影响和分析窗口长度的影响，即使进行最佳处理，时频分析的精度仍然较低。因此，我们创新性地开发了基于树突学习的引力波预测模型（DLGWP），从时间序列预测的角度预测GW的频率变化。它有效解决了使用时频分析方法后GW信号损失和分析不完整的问题。与其他最先进的时间序列预测模型相比，结果表明DLGWP具有优越的性能，是一种有效的GW时频预测模型。性能提升在传统模型无法有效抑制噪声或丢失关键信号细节的情况下尤为显著。通过准确预测GW数据中的频率变化，我们的模型有助于提高GW检测的整体精度，使其成为未来研究的有价值的工具。
Keyword:
Gravitational wave
Time-frequency prediction
Dendritic learning
Neural network
Signal decomposition-based loess

期刊

Information Sciences 封面图
Information Sciences
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6.8
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540
被引数:
6.2W

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university of toyama
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Jiangsu Ocean University
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qingdao university of science & technology
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