基于主成分分析(PCA)和支持向量机(SVM)的耙吸挖泥船工况分析

土质信息对于耙吸挖泥船的挖掘与装舱过程的控制策略具有重要意义，但耙吸挖泥船的耙头无法直接感知土质信息。基于实船数据，通过主成分分析（PCA）对非线性耦合的疏浚数据进行线性可视化和特征分析，对土质信息与工况信息的关联性进行研究。采用支持向量机（SVM）构建分类器，对土质工况进行分类识别。结果表明，该方法能够有效识别不同的土质工况，实现了土质信息的间接感知。

Analysis on working condition of suction hopper dredger based on PCA and SVM

The soil information is great significance to the control strategy of the excavation and loading process of the suction hopper dredger,but the suction hopper dredger cannot directly sense the soil information.We conduct linear visualization and feature analysis of nonlinear coupled dredging data based on real ship data through principal component analysis(PCA),and study the correlation between soil information and working condition information.The support vector machine (SVM) is used to construct the classifier to classify and identify the soil conditions.The results show that the method can effectively identify different working conditions,and realizes the indirect perception of soil information.