摘要
为实现船舶能效的智能优化,从船舶通航环境出发,研究船舶通航环境的智能识别方法。基于所获得的船舶通航环境大数据,建立基于改进K均值聚类算法的船舶通航环境类别知识库,设计相关系数加权的K近邻算法,实现船舶通航环境的智能识别。实例分析结果表明,此基于改进K近邻算法的智能识别方法的识别准确率可达97.25%,相对于未改进的K近邻算法,准确率提高7.81%。所提出的智能识别方法可为基于通航环境智能识别的船舶分段航速智能优化方法的研究奠定基础。
In order to realize the intelligent optimization of ship energy efficiency,starting from the ship navigation environment,the intelligent identification method of the ship navigation environment is studied.Based on the obtained ship navigation environment big data,a knowledge base of ship navigation environment categories based on the improved K-means clustering algorithm is established,and a K-nearest neighbor algorithm weighted by correlation coefficients is designed to realize the intelligent identification of ship navigation environment.The example analysis results show that,the identification accuracy of the intelligent recognition method based on the improved K-nearest neighbor algorithm can reach 97.25%,which can increase the accuracy rate by 7.81%compared with the unimproved K-nearest neighbor algorithm.The proposed intelligent identification method can lay the foundation for the research on the intelligent optimization method of ship segmentation speed based on the intelligent identification of navigation environment.
作者
王壮
李嘉源
黄连忠
王凯
姜雅乔
马冉祺
WANG Zhuang;LI Jiayuan;HUANG Lianzhong;WANG Kai;JIANG Yaqiao;MA Ranqi(Marine Engineering College, Dalian Maritime University, Dalian 116026, Liaoning, China)
出处
《上海海事大学学报》
北大核心
2020年第3期36-41,共6页
Journal of Shanghai Maritime University
基金
国家自然科学基金(51909020)
中央高校基本科研业务费专项资金(3132019194)
无人船协同创新研究院种子基金(3132019009)
辽宁省自然科学基金(2019-BS-023)
长江航道局科技项目(201930004)。
关键词
通航环境
K均值聚类
K近邻算法
智能识别
navigation environment
K-means clustering
K-nearest neighbor algorithm
intelligent identification