径向基神经网络在水下船体防腐中的应用

采用改进型径向基函数神经网络来预测水下船体电位,从而达到船体防腐的目的。首先,分析采用无单元法求解问题的优越性;然后,介绍径向基函数神经网络的原理,通过建立水下船体的数学模型来获得船体电位,采用层次凝聚算法和K-均值聚类算法来确定径向基函数神经网络的中心节点,从而建立神经网络;最后,对神经网络的预测能力进行MATLAB仿真检验。结果表明,采用改进型的径向基函数神经网络来预测船体电位获得了很高的精度,对于船体防腐保护达到了很好的效果。

The Application of RBF Neural Network to the UnderwaterHull Antisepsis

This paper uses the improved RBF neural network to predict the underwater hull potential, so as to achieve the goal of hull antisepsis. First of all, analyze the advantage of adopting meshless method to solve the problem, and then introduce the principle of RBF neural network. Obtain the hull potential By establishing the mathematical model of underwater ship hull. In addition, determine the center of RBF neural network node by adopting agglomerative hierarchical clustering algorithm and k-means clustering algorithm, so as to establish a neural network. Finally, perform the simulation test on the neural network predictive ability using MATLAB, and the results prove that it shows high accuracy to predict hull potential using modified RBF neural network, and bring about a striking effect on hull antisepsis.