舰船在航行过程中受到自然因素、自身因素和水域因素影响易发生安全事故，造成严重程度不同的人员伤亡、直接经济损失和海洋环境污染损失。为保证舰船航行的安全性，采用极限学习机算法对安全性进行预测，借助极限学习机算法泛化性能好、学习速度快等优势，准确得出最优解，提高航行安全性影响因素识别的准确率。本文概述改进极限学习机算法与网络训练流程，提出改进极限学习机算法在舰船安全性预测中的预测流程与模型构建。仿真实验表明，本文提出的算法能够提高舰船安全性识别的准确性和时效性。
In the course of navigation, ships are prone to safety accidents due to natural factors, their own factors and water factors, resulting in casualties, direct economic losses and marine environmental pollution losses of varying degrees of severity. In order to ensure the safety of ship navigation, the extreme learning machine algorithm should be used to predict the safety. With the help of the advantages of the extreme learning machine algorithm, such as good generalization performance and fast learning speed, the optimal solution can be accurately obtained, and the navigation safety can be improved. The accuracy of factor identification. This paper summarizes the improved extreme learning machine algorithm and network training process, and proposes the prediction process and model construction of the improved extreme learning machine algorithm in ship safety prediction. Simulation experiments show that the algorithm proposed in this paper can improve the accuracy of ship safety identification. performance and effectiveness.
2022,44(16): 151-154 收稿日期：2022-01-14
DOI：10.3404/j.issn.1672-7649.2022.16.032
分类号：U665
基金项目：江西省教育厅科学技术研究项目（GJJ204203）
作者简介：胡晓辉(1977-)，女，硕士，副教授，主要从事计算机软件开发和研究
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