|Table of Contents|

Granular computing:a new method of intelligent modelingfor big data fusion(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年04期
Page:
503-
Research Field:
Publishing date:

Info

Title:
Granular computing:a new method of intelligent modelingfor big data fusion
Author(s):
Yao Fuguang1Zhong Xianxin2Zhou Jingchao3
1.Information Center,Chongqing University of Education,Chongqing 400065,China; 2.Key Laboratory of Optoelectronics Technology and Systems of the Education Ministryof China,Chongqing University,Chongqing 400044,China; 3.School of Engineering,Rutgers University,New Jersey,08901 USA
Keywords:
complex system big data fusion granular computing deep learning artificial intelligence cognitive computing
PACS:
TP183
DOI:
10.14177/j.cnki.32-1397n.2018.42.04.017
Abstract:
Big data fusion intelligence is a core problem of data science and engineering. In the era of big data,as new generation of artificial intelligence technology,deep learning is applied to the field of complex systems,facing new opportunities and serious technical challenges. This paper first analyzes the connotation of the problem of big data fusion. The new visual angle of deep learning technology research and the new technology dynamics of granular computing in information fusion are analyzed from the point of view of complex system. The feasibility of the fusion of granular computing and deep learning is deduced. Finally,the architecture of large data fusion intelligent modeling particle computing and processing for complex system cognition research is discussed. The research is a meaningful work for development method of the system management and control research.

References:

[1] 刘全,翟建伟,章宗长,等. 深度强化学习综述[J]. 计算机学报,2017,41(1):1-27.
Liu Quan,Zhai Jianwei,Zhang Zongzhang,et. al. A survey on deep reinforcement learning[J]. Chinese Journal of Computers,2017,41(1):1-27.
[2]程浩,赵瑾,刘俊友. 基于信息融合的贝叶斯网络毁伤评估方法[J]. 四川兵工学报,2015,36(4):104-107.
Cheng Hao,Zhao Jin,Liu Junyou. Damage effect assessment approach study of Bayesion networks based on information fusion[J]. Journal of Sichuan Ordnance,2015,36(4):104-107.
[3]安进,徐廷学,李志强,等. 基于数据融合的某型装备品质状态评估[J]. 兵器装备工程学报,2017,38(6):13-18.
An Jin,Xu Tingxue,Li Zhiqiang,et. al. Quality condition evaluation of certain equipment based on data fusion[J]. Journal of Ordnance Equipment Engineering,2017,38(6):13-18.
[4]Dong X L,Gabrilovich E,Heitz G,et al. From data fusion to knowledge fusion[J]. The VLDB Endowment,2014,7(10):881-892.
[5]白亮,郭金林,老松杨. 基于深度认知神经网络的跨媒体情报大数据智能处理技术[J]. 指挥与控制学报,2016,2(4):345-352.
Bai Liang,Guo Jinlin,Lao Songyang. Cross-media intelligence processing based on deep cognition nerrrrrrural networks[J]. Journal of Command and Contral,2016,2(4):345-352.
[6]常天庆,王全东,郝娜,等. 基于信息融合的目标管理与被动定位方法[J]. 系统工程与电子技术,2017,39(9):1921-1933.
Chang Tianqing,Wang Quandong,Hao Na,et al. Target management and passive location based on information fusion[J]. Systems Engineering and Electronics,2017,39(9):1921-1933.
[7]吴荣春. 军事信息系统中信息融合技术研究[D]. 成都:电子科技大学研究生院,2016.
[8]齐金山,梁循,李志宇,等. 大规模复杂信息网络表示学习:概念、方法与挑战. 2017,Vol. 40,在线出版号No. 178.
Qi Jinshan,Liang Xun,Li Zhiyu,et al. Representation learning of large-scale complex information network:concepts,methods and challenges. 2017,Vol. 40,Online Publishing No. 178.
[9]林海伦,王元卓,贾岩涛,等. 面向网络大数据的知识融合方法综述[J]. 计算机学报,2017,40(1):1-27.
Lin Hailun,Wang Yuanzhuo,Jia Yantao,et al. Network big data oriented knowledge fusion methods:a survey[J]. Chinese Journal of Computers,2017,40(1):1-27.
[10]Rabinowitz N C,Perbet F,Francis H S,et al. Machine theory of mind[J]. Proceedings of Machine Learning Research,2018,80:4215-4224.
[11]Sabour S,Frasst N,Hinton G E. Dynamic routing between capsules[C]//31th Conference on Neural Information Processing Systems(NIPS). Long Beach,CA,USA:IEEE,2017.
[12]Hu Hong,Pang Liang,Shi Zhongzhi. Image matting in the perception granular deep learning[J]. Knowledge-based Systems,2016,102:51-63.
[13]Li H X,Zhang L B,Zhou X Z,et al. Cost-sensitive sequential three-way decision modeling using a deep neural network[J]. International Journal of Approximate Reasoning,2017,85:68-78.
[14]徐计,王国胤,于洪. 基于粒计算的大数据处理[J]. 计算机学报,2015,38(8):1497-1517.
Xu Ji,Wang Guoyin,Yu Hong. Review of big data processing based on granular computing[J]. Chinese Journal of Computers,2015,38(8):1497-1517.
[15]Chen C L,Zhang C Y. Data-intensive applications,challenges,techniques and technologies:a survey on big data[J]. Inform Sci,2014,275:314-347.
[16]梁吉业,钱宇华,李德玉,等. 大数据挖掘的粒计算理论与方法[J]. 中国科学:信息科学,2015,45(11):1355-1369.
Liang Jiye,Qian Yuhua,Li Deyu,et. al. Theory and method of granular computing for big data mining[J]. Scientia Sinica Informationis,2015,45(11):1355-1369.
[17]苗夺谦,张清华,钱宇华,等. 从人类智能到机器实现模型——粒计算理论与方法[J]. 智能系统学报,2016,11(6):743-757.
Miao Duoqian,Zhang Qinghua,Qian Yuhua,et al. From human intelligence to machine implementation model:theories and apllications based on granular computing[J]. CAAI Transactions on Intelligent Systems,2016,11(6):743-757
[18]Chen C L,Zhang C Y. Data-intensive applications,challenges,techniques and technologies:a survey on big data[J]. Information Sciences,2014,275:314-347.
[19]Xu Weihua,Yu Jianhang. A novel approach to information fusion in multi-source dataswts:a granular computing viewpoint[J]. Information Sciences,2017,378:410-423.
[20]Li F J,Qian Y H,Wang J T,et al. Multigranulation information fusion:A Dempster-Shafer evidence theory-based clustering ensemble method[J]. Information Sciences,2017,378:389-409.
[21]Huang Chenchen,Li Jinhai,Mei Changlin,et al. Three-way concept learning based on cognitive operators:An information fusion viewpoint[J]. International Journal of Approximate Reasoning,2017,83:218-242.
[22]Tang Y Q,Fan M,Li J H. An information fusion technology for triadic decision contexts[J]. International Journal of Machine Learning and Cybernetics,2016,7(1):13-24.
[23]李蒙,朱卫纲,陈维高. 基于机器学习的雷达辐射源识别研究综述[J]. 兵器装备工程学报,2016,37(9):171-175.
Li Meng,Zhu Weigang,Chen Weigao. Study of radar emitter identification based on machine learning[J]. Journal of Ordnance Equipment Engineering,2016,37(9):171-175.
[24]杨习贝,杨静宇. 邻域系统粗糙集模型[J]. 南京理工大学学报,2012,36(2):291-295.
Yang Xibei,Yang Jingyu. Rough set model based on neigborhood system[J]. Journal of Nanjing University of Science and Technology,2012,36(2):291-295.
[25]付耀文,杨威,庄钊文. 证据建模研究综述[J]. 系统工程与电子技术,2013,35(6):60-71.
Fu Yaowen,Yang Wei,Zhuang Zhaowen. Review on evidence modeling[J]. Systems Engineering and Electronics,2013,35(6):60-71.
[26]郭强,何友,李新德. 一种快速DSmT-DS近似推理融合方法[J]. 电子与信息学报,2015,37(9):2040-2046.
Guo Qiang,He You,Li Xinde. Fast DSmT-DS approximate reasoning method[J]. Journal of Electronics & Information Technology,2015,37(9):2040-2046.
[27]郭强,何友. 基于云模型的DSm证据建模及雷达辐射源识别方法[J]. 电子信息学报,2015,37(8):1779-1885.
Guo Qiang,He You. DSm evidence modeling and radar emitter fusion recognition method based on cloud model[J]. Journal of Electronics & Information Technology,2015,37(8):1779-1885.

Memo

Memo:
-
Last Update: 2018-08-30