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计量学报  2024, Vol. 45 Issue (12): 1824-1831    DOI: 10.3969/j.issn.1000-1158.2024.12.11
  力学计量 本期目录 | 过刊浏览 | 高级检索 |
基于改进Resnet-LSTM模型的系泊缆仿真张力预测
张火明1,黄敏1,陆萍蓝2
1.中国计量大学 浙江省流量计量技术重点实验室,浙江 杭州 310018
2.中国计量大学 工程训练中心,浙江 杭州 310018
Simulation Mooring Lines Tension Prediction Based onImproved Resnet-LSTM Model
ZHANG Huoming1,HUANG Min1,LU Pinglan2
1. Zhejiang Provincial Key Laboratory of Flow Measurement Technology, China Jiliang University, Hangzhou, Zhejiang 310018, China
2. Engineering Training Center, China Jiliang University, Hangzhou, Zhejiang 310018, China
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摘要 对海洋浮式平台系泊系统在复杂多变的作业环境受到的线性、非线性作用力进行了预测,预测过程中对长短周期记忆(LSTM)单模型预测网络隐藏层数、迭代次数和学习速率做了优化。提出了具有可变卷积和小波基激活函数的多层特征提取特性和变阈值残差收缩预测功能的混合预测模型,对平台运动响应多点系泊系统整体受力进行非线性映射,分析了多点系泊缆模型,得到了风浪流联合作用下的系泊缆张力值。使用LSTM单模型、Resnet-LSTM混合模型和改进混合模型对系泊力仿真数据集进行训练预测。结果显示:采用Resnet-LSTM混合模型预测准确度可达0.9974,使用可变卷积改进的Resnet-LSTM预测效果优于未改进模型,各项网络参数和预测指标得以优化。证明基于Resnet-LSTM的改进混合预测模型应用在多点系泊系统张力非线性时序特征预测应用方面具有提升网络性能的作用。
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张火明
黄敏
陆萍蓝
关键词 力学计量系泊力多点系泊系统改进Resnet-LSTM模型张力预测    
Abstract:The linear and nonlinear forces on an offshore floating platform mooring system in a complex and variable operating environment are predicted, and the number of hidden layers, iterations, and learning rate of the long-short cycle memory (LSTM) single-model prediction network are optimized during the prediction process. A hybrid prediction model with multilayer feature extraction characteristics of variable convolution and wavelet-based activation functions and variable threshold residual shrinkage prediction function is proposed to nonlinearly map the overall forces of the multi-point mooring system in response to the platform motion, analyze the model of multi-point mooring cables, and obtain the value of the mooring cable tension under the combined action of wind and wave currents. The mooring force simulation dataset is trained for prediction using LSTM single model, Resnet-LSTM hybrid model and improved hybrid model. The results show and the network parameters and prediction indicators are optimized, that the prediction accuracy can be as high as 0.9974 using the Resnet-LSTM hybrid model, and the improved Resnet-LSTM prediction using variable convolution is better than the unimproved model. It is demonstrated that the application of the improved hybrid prediction model based on Resnet-LSTM has the effect of improving the network performance in the application of nonlinear time-series feature prediction of tension in multipoint mooring systems.
Key wordsmechanica metrology;mooring lines tension    multi-points mooring system    improved Resnet-LSTM model    tension prediction
收稿日期: 2023-04-11      发布日期: 2024-12-18
PACS:  TB931  
基金资助:浙江省自然科学基金(LY19E090004)
作者简介: 张火明(1976-),男,湖北武穴人,中国计量大学副教授,主要从事海洋工程流体力学等方面的研究。Email: zhm102018@163.com
引用本文:   
张火明,黄敏,陆萍蓝. 基于改进Resnet-LSTM模型的系泊缆仿真张力预测[J]. 计量学报, 2024, 45(12): 1824-1831.
ZHANG Huoming,HUANG Min,LU Pinglan. Simulation Mooring Lines Tension Prediction Based onImproved Resnet-LSTM Model. Acta Metrologica Sinica, 2024, 45(12): 1824-1831.
链接本文:  
http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2024.12.11     或     http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2024/V45/I12/1824
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