Main Article Content
This paper mainly discusses the method for multi-source information fusion, and gives an algorithm for multi-source information fusion based on fuzzy partial order relation.The key of achieving information fusion is to make all the elements comparable. First, we will transfer the established fuzzy partial order into total order to get the good or bad order of the subjects being evaluated and the most important information, furthermore a new system is obtained. Second, we get the algorithms of the information fusion. Finally, we test the feasibility and effectiveness of the algorithms via an example.
Zhang Wenxiu, Wu Weizhi, Liang Jiye, et al. Rough set theory and methods. Science Press;2001.
Jinhai Li, Yuejin Lu. Fast Attribute reduction algorithm for decision system. University of Electronic Science and Technology. 2007;36(6):1237-1240.
Lizhen Qin, Bingxue Yao, Jinhai Li. Complete covering reduction algorithm based on information quantity. Computer Science. 2012;39(10):235-239.
Zadeh LA. Fuzzy sets.inf. Control. 1965;8(3):338-353.
Haizhen Wang, Zuozheng Lian. Research on the method of fault diagnosis based on Modulo and rough set theory. Journal of the Qiqihar University. 2006(01):38-40.
Qinghua Hu, Daren Yu, Congxin Wu. Fuzzy preference rough sets. IEEE Conference on Granular Computing. 2008:62-76.
Qinghua Hu, Daren Yu, Zongxia Xie. Fuzzy probabilistic approximation spaces and their information measures. IEEE Transactions on Fuzzy Systems. 2006;14(2):191-201.
Limei Guo, Dayong Luo. Intelligent information fusion based on fuzzy and partial order relation for decision-making assessment. Sensors and Microsystems. 2008;27(11):18-20.
Fai Wong Chan. Research on decision rule mining in multi-source information systems. Zhangzhou Normal University; 2018.
Weihua Xu, Mengmeng Li, Xizhao Wang. Information fusion based on information entropy in fuzzy multi-source incomplete information system[J]. Springer Berlin Heidelberg. 2017;19(4).
Leng Han, Peng Dai, Yuan Yang, Qing Wang. Indoor location method based on multi-source Information Fusion. Sensors and Microsystems. 2020;39(07):21-24.
Longshuai Pan, Jianping Gao, Zhe Song, Jianguo Xi. Vehicle speed prediction method based on multi-source information fusion and vehicle energy management. Proceedings of the Henan University of Science and Technology. 2020;41(06):23-31+4-5.
Baohang Guo, Shirong Zhang, Chao Wang, Duanyang Li, et. al. Structural safety risk assessment method of long-distance water diversion project based on multi-source information fusion. Science of hydroelectric Energy. 2020;38(06):84-88.
Jintao Yao, Jiang Li, Peng Ma. A multi-source navigation information fusion localization method J. Modern Navigation. 2020;11(03):192-196+200.
Lei Zhao. Simulation of distributed multi-source information fast fusion in network video. Computer Simulation. 2020;37(06):173-176.
Changjun Zha, Sebastian Anita. Special object recognition based on sparse representation in multisource data fusion samples. Hindawi; 2020.
Qianming Li, Bohong Zheng, Bing Tu, Yusheng Yang, Zhiyuan Wang, Wei Jiang, Kai Yao, Jiawei Yang. Refining urban built-up area via multi-source data fusion for the analysis of dongting lake eco-economic zone spatiotemporal expansion. MDPI. 2020;12(11).
Li Yang, Jia Honglei, Qi Jiangtao, Sun Huibin, Tian Xinliang, Liu Huili, Fan Xuhui. An acquisition method of agricultural equipment roll angle based on multi-source information fusion. Pubmed. 2020;20(7)