Application of Ensemble Learning Algorithms on Ship Radiation Noise Prediction

QIANG Yiming; CHEN Yihong; PEI Yuqing; PANG Yezhen; XIE Shuo

doi:10.11993/j.issn.2096-3920.2025-0111

Volume 33 Issue 6

Dec 2025

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(6): 988-994

QIANG Yiming, CHEN Yihong, PEI Yuqing, PANG Yezhen, XIE Shuo. Application of Ensemble Learning Algorithms on Ship Radiation Noise Prediction[J]. Journal of Unmanned Undersea Systems, 2025, 33(6): 988-994. doi: 10.11993/j.issn.2096-3920.2025-0111

Citation:

QIANG Yiming, CHEN Yihong, PEI Yuqing, PANG Yezhen, XIE Shuo. Application of Ensemble Learning Algorithms on Ship Radiation Noise Prediction[J]. Journal of Unmanned Undersea Systems, 2025, 33(6): 988-994. doi: 10.11993/j.issn.2096-3920.2025-0111

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Application of Ensemble Learning Algorithms on Ship Radiation Noise Prediction

doi: 10.11993/j.issn.2096-3920.2025-0111

QIANG Yiming^{1, 3
,},
CHEN Yihong^{1, 2
,
,},
PEI Yuqing^{1, 2},
PANG Yezhen^{1, 2},
XIE Shuo¹

1.
China Ship Scientific Research Center, Wuxi 214082, China
2.
Ship Vibration and Noise Key Laboratory, Wuxi 214082, China
3.
School of Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China

Received Date: 2025-08-22
Accepted Date: 2025-09-15
Rev Recd Date: 2025-09-12

Available Online: 2025-11-26

Abstract

Abstract

The radiation noise generated by multiple vibration sources on ships can seriously affect their comfort and stealth performance. Accurately predicting the radiation noise level and distribution can provide crucial support for the vibration and noise reduction optimization during the ship design stage. In response to the problem of numerous vibration sources in ships and the complex noise radiation mechanism, this paper first employed the random forest and gradient boosting tree algorithms from the ensemble learning algorithms to conduct rapid prediction of the 1/3 octave band noise sound pressure level under different operating conditions and at different measurement points. The prediction results were then compared and verified with those of the Bayesian ridge regression model. The verification results of the four test conditions show that the ensemble learning algorithm outperforms the Bayesian ridge regression in all three conditions, with an average absolute error of less than 5 dB. Further optimization of the above model is conducted by constructing the basic units of the ensemble learning algorithm and the linear algorithm at different levels and combining them to form a joint radiation noise prediction scheme. Its accuracy is improved by 1.5 dB compared to the single ensemble learning algorithm. The ensemble learning algorithm and the joint prediction scheme proposed in this paper can provide effective technical tools for the rapid and accurate analysis of ship radiation noise.
- ship radiation noise,
- noise prediction,
- ensemble learning algorithm,
- vibration and noise reduction

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References(19)

References

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