Uncertainty Representation of Ocean Fronts Based on Fuzzy-Rough Set Theory

被引:0
作者
XUE Cunjin ZHOU Chenghu SU Fenzhen and ZHANG Dandan The Marine GISs Center of State Key Laboratory of Resources and Environment Information System Chinese Academy of Sciences Beijing P R China Graduate School of the Chinese Academy of Sciences Beijing P R China [1 ,2 ,1 ,1 ,1 ,2 ,1 ,100101 ,2 ,100039 ]
机构
关键词
fuzzy-rough set; upper approximate sets; lower approximate sets; ocean fronts; uncertainties;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Analysis of ocean fronts’ uncertainties indicates that they result from indiscernibility of their spatial position and fuzzi- ness of their intensity. In view of this, a flow hierarchy for uncertainty representation of ocean fronts is proposed on the basis of fuzzy-rough set theory. Firstly, raster scanning and blurring are carried out on an ocean front, and the upper and lower approximate sets, the indiscernible relation in fuzzy-rough theories and related operators in fuzzy set theories are adopted to represent its uncer- tainties, then they are classified into three sets: with members one hundred percent belonging to the ocean front, belonging to the ocean front’s edge and definitely not belonging to the ocean front. Finally, the approximate precision and roughness degree are util- ized to evaluate the ocean front’s degree of uncertainties and the precision of the representation. It has been proven that the method is not only capable of representing ocean fronts’ uncertainties, but also provides a new theory and method for uncertainty representation of other oceanic phenomena.
引用
收藏
页码:131 / 136
页数:6
相关论文
共 50 条
[31]   Fuzzy-Rough-Set-Based Active Learning [J].
Wang, Ran ;
Chen, Degang ;
Kwong, Sam .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2014, 22 (06) :1699-1704
[32]   Knowledge Discovery Based on Fuzzy Rough Set Theory by Inclusion Degrees for Space Load Forecasting [J].
Li, Weiguo ;
Li, Hong ;
Xiong, Haoqing ;
Xu, Guoyi ;
Zou, Jiangfeng ;
Yu, Weicheng .
2009 INTERNATIONAL CONFERENCE ON SUSTAINABLE POWER GENERATION AND SUPPLY, VOLS 1-4, 2009, :2340-+
[33]   Rough set theory for the incomplete interval valued fuzzy information systems [J].
Gong, Zengtai ;
Tao, Lei .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2014, 26 (02) :889-900
[34]   Extracting Information in Agricultural Data Using Fuzzy-Rough Sets Hybridization and Clonal Selection Theory Inspired Algorithms [J].
Lasisi, Ayodele ;
Ghazali, Rozaida ;
Deris, Mustafa Mat ;
Herawan, Tutut ;
Lasisi, Fola .
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2016, 30 (09)
[35]   Condensed fuzzy nearest neighbor methods based on fuzzy rough set technique [J].
Zhai, Junhai ;
Zhai, Mengyao ;
Kang, Xiaomeng .
INTELLIGENT DATA ANALYSIS, 2014, 18 (03) :429-447
[36]   Fuzzy rough set based attribute reduction for information systems with fuzzy decisions [J].
He, Qiang ;
Wu, Congxin ;
Chen, Degang ;
Zhao, Suyun .
KNOWLEDGE-BASED SYSTEMS, 2011, 24 (05) :689-696
[37]   Missing data imputation for traffic flow based on combination of fuzzy neural network and rough set theory [J].
Tang, Jinjun ;
Zhang, Xinshao ;
Yin, Weiqi ;
Zou, Yajie ;
Wang, Yinhai .
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 25 (05) :439-454
[38]   New Fuzzy Rough Set Models Based on Implication Operators [J].
Zhang, Xiaoyi ;
Liu, Qi ;
Zhang, Chao ;
Zhan, Jianming .
COMPUTATIONAL & APPLIED MATHEMATICS, 2025, 44 (01)
[39]   Nearest Neighbor Condensation Based on Fuzzy Rough Set for Classification [J].
Pan, Wei ;
She, Kun ;
Wei, Pengyuan ;
Zeng, Kai .
ROUGH SETS AND KNOWLEDGE TECHNOLOGY, RSKT 2014, 2014, 8818 :432-443
[40]   Dynamic interaction feature selection based on fuzzy rough set [J].
Wan, Jihong ;
Chen, Hongmei ;
Li, Tianrui ;
Yang, Xiaoling ;
Sang, Binbin .
INFORMATION SCIENCES, 2021, 581 :891-911