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首页> 外文期刊>Applied Mechanics and Engineering >ANFIS AND NEURAL NETWORK FOR MODELING AND PREDICTION OF SHIP SQUAT IN SHALLOW WATERS: A NEW APPROACH
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ANFIS AND NEURAL NETWORK FOR MODELING AND PREDICTION OF SHIP SQUAT IN SHALLOW WATERS: A NEW APPROACH

机译：浅水区船舶深蹲建模与预测的ANFIS和神经网络：一种新方法

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摘要

The reduction of the distance between ship floor and seabed, while the ship is moving forward, is called squat. In this research, squat is determined for vessels with Series-60 hull forms in various depths by experimental methods and then different numerical methods are employed for squat modeling. For this reason, a set of facilities for testing the ship movement in shallow waters is prepared. A series of models of the vessel is manufactured and many tests are carried out. The aim of the present study is to demonstrate the usefulness of an adaptive-network-based fuzzy inference system (ANFIS) for modeling and predicting the squat parameter for ships in shallow waters. It is also shown how dimensionless squat (S~*) varies with the variation of important parameters, namely: block coefficient (CB), dimensionless distance between the seabed and ship floor (S) and hydraulic Froude Number (Fn_h). The results obtained through the ANFIS are also compared with those of a multiple linear regression and GMDH-type neural network with multi-layered feed forward back propagation algorithm. The results show that the ANFIS-based squat has higher predictability function than other numerical methods.
机译：船舶前进时，减小船底与海床之间的距离称为蹲坐。在这项研究中，通过实验方法确定具有不同深度的Series-60船体形式的船舶的下蹲，然后采用不同的数值方法进行下蹲建模。因此，准备了一套用于测试船舶在浅水中运动的设施。制造了一系列的容器模型，并进行了许多测试。本研究的目的是证明基于自适应网络的模糊推理系统（ANFIS）对浅水区船舶的下蹲参数进行建模和预测的有用性。还显示了无因次蹲点（S〜*）如何随重要参数的变化而变化，这些重要参数包括：阻滞系数（CB），海床与船底之间的无因次距离（S）和水力弗洛德数（Fn_h）。通过ANFIS获得的结果也与带有多层前馈传播算法的多元线性回归和GMDH型神经网络的结果进行了比较。结果表明，基于ANFIS的深蹲具有比其他数值方法更高的可预测性功能。

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