Chinese Journal of Ship Research

非周期系统的局域化现­象研究

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5由表 可知,两种建模方案的决定系­数都趋1,表明对于输入-输出的真实映射关系都­取近于得了较好的拟合;加入前处理环节之后,相比初始的代理模型构­建方案,修改后的代理模型构建­方案的标准化均方根误­差和标准化最大绝对误­差分9.80% 15.43%别减小了 和 ,整体误差和局部误差都­得到了一定程度的减小。

4结论

本文以一水下结构物典­型平台基座为研究对象,计算分析其阻抗曲线特­性,通过代理模型对该结构­的参数输入-阻抗输出映射关系进行­拟合,研究结果表明: 1)相比支持向量机代理模­型、径向基代理模BP神经­网络代理模型,Kriging型和 代理模型对该问题具备­更好的适用性; 2)结合工程实际和阻抗特­性曲线特征,提出的基座阻抗样本点­响应数据前处理方法,可以有效地提高代理模­型的拟合精度,标准化均方根误9.80%差和标准化最大绝对误­差分别减小了 和15.43%。本文提出的代理模型构­造方案及精度分析结果,可用于指导实际工程中­基座结构阻抗特性的快­速预报。

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 ??  ?? 图6 代理模型性能对比流程­图Fig.6 The flow chart of performanc­e comparison of surrogate models
图6 代理模型性能对比流程­图Fig.6 The flow chart of performanc­e comparison of surrogate models

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