[1]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.[doi:10.11992/tis.201612014]
 MIAO Duoqian,ZHANG Qinghua,QIAN Yuhua,et al.From human intelligence to machine implementation model: theories and applications based on granular computing[J].CAAI Transactions on Intelligent Systems,2016,11(6):743-757.[doi:10.11992/tis.201612014]
点击复制

从人类智能到机器实现模型——粒计算理论与方法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年6期
页码:
743-757
栏目:
出版日期:
2017-01-20

文章信息/Info

Title:
From human intelligence to machine implementation model: theories and applications based on granular computing
作者:
苗夺谦1 张清华2 钱宇华3 梁吉业3 王国胤2 吴伟志4 高阳5 商琳5 顾沈明4 张红云1
1. 同济大学 嵌入式系统与服务计算教育部重点实验室, 上海 201804;
2. 重庆邮电大学 计算智能重庆市重点实验室, 重庆 400065;
3. 山西大学 计算智能与中文信息处理教育部重点实验室, 山西 太原 030006;
4. 浙江海洋大学 浙江省海洋大数据挖掘与应用重点实验室, 浙江 舟山 316022;
5. 南京大学 软件新技术国家重点实验室, 江苏 南京 210093
Author(s):
MIAO Duoqian1 ZHANG Qinghua2 QIAN Yuhua3 LIANG Jiye3 WANG Guoyin2 WU Weizhi4 GAO Yang5 SHANG Lin5 GU Shenming4 ZHANG Hongyun1
1. Key Laboratory of Embedded System & Service Computing Ministry of Education, Tongji University, Shanghai 201804, China;
2. Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
3. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, China;
4. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan 316022, China;
5. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
关键词:
人工智能大数据不确定性粒计算多粒度粗糙集商空间模糊集云模型三支决策
Keywords:
artificial intelligencebig datauncertaintygranular computingmulti-granulationrough setsquotient spacecloud modelthree-way decisions
分类号:
TP391
DOI:
10.11992/tis.201612014
摘要:
人工智能是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学,是对人的意识、思维过程的模拟。粒计算是当前智能信息处理领域中一种新的概念和计算范式,是研究基于多层次粒结构的思维方式、复杂问题求解、信息处理模式及其相关理论、技术和工具的方法论。本文首先分析了人工智能模拟人脑智能的粒计算模式与方法,其次总结了粗糙集、商空间、模糊集、云模型、三支决策等几种典型的粒计算基本构架与数学模型,然后分析知识的多粒度解析表示与不确定性度量的研究现状,最后展望了粒计算求解模式在大数据时代所面临的机遇与挑战。
Abstract:
Artificial intelligence is a new science of researching and developing theories, methods and technologies to simulate and extend the human intelligence, and is regarded as a simulation of human consciousness and thought processes. Granular computing is a novel concept and a new computing paradigm in the current area of intelligent information processing. It is also a multi-granulation methodology of relevant theories, technologies and tools, which are used to research multi-level thought modes, to solve complex problems and to develop information processing models. First, the related granular computing models or methods, by which artificial intelligence simulates human intelligence, were analyzed in this paper. Also, several classical basic structures and mathematical models on granular computing were briefly summarized. Then, both multi-granulation representations and uncertainty measurements on knowledge were reviewed. Finally, the future opportunities and challenges of solving models using granular computing in the era of big data were discussed and prospected.

参考文献/References:

[1] ZADEH L A. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J]. Fuzzy sets and systems, 1997, 90(2):111-127.
[2] http://link.springer.com/journal/41066.
[3] http://www.nsfc.gov.cn/nsfc/cen/xmzn/2016xmzn/04/06xx/004.html.
[4] http://dblp.uni-trier.de/db/conf/rsfdgrc/.
[5] HU Qinghua, MI Jusheng, CHEN Degang. Granular computing based machine learning in the era of big data[J]. Information Sciences, 2017, 378:242-243.
[6] 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.
[7] ZHANG Bo, ZHANG Ling. Theory and applications of problem solving[M]. New York:North Holland, 1992.
[8] FRIEDMAN N. Inferring cellular networks using probabilistic graphical models[J]. Science, 2004, 303(5659):799-805.
[9] CLAUSET A, MOORE C, NEWMAN M E J. Hierarchical structure and the prediction of missing links in networks[J]. Nature, 2008, 453(7191):98-101.
[10] AHN Y Y, BAGROW J P, LEHMANN S. Link communities reveal multiscale complexity in networks[J]. Nature, 2010, 466(7307):761-764.
[11] WU W Z, LEUNG Y. Theory and applications of granular labelled partitions in multi-scale decision tables[J]. Information sciences, 2011, 181(18):3878-3897.
[12] GEOFFREY E, Ruslan R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[13] HU Hong, PANG Liang, SHI Zhongzhi. Image matting in the perception granular deep learning[J]. Knowledge-based systems. 2016, 102:51-63.
[14] ZADEH L A. Fuzzy sets[J]. Information and control, 1965, 8(3):338-353.
[15] SHANNON C E. A mathematical theory of communication[J]. The bell system technical journal, 1948, 27(3):379-423.
[16] WANG Guoyin, HU Jun, ZHANG Qinghua, et al. Granular computing based data mining in the views of rough set and fuzzy set[C]//Proceedings of 2008 IEEE International Conference on Granular Computing. Hangzhou, China:IEEE, 2008:67-78.
[17] ZADEH L A. Fuzzy sets and information granularity[C]//GUPTA N, RAGADE R, YAGER R. Advances in Fuzzy Set Theory and Applications. Amsterdam:North-Holland, 1979:3-18.
[18] ZADEH L A. Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems[J]. Soft computing, 1998, 2(1):23-25.
[19] PAWLAK Z. Rough sets[J]. International journal of computer & information sciences, 1982, 11(5):341-356.
[20] 张钹, 张铃. 问题求解理论及应用[M]. 北京:清华大学出版社, 1990.
[21] 李德毅, 孟海军, 史雪梅. 隶属云和隶属云发生器[J]. 计算机研究与发展, 1995, 32(6):15-20. LI Deyi, MENG Haijun, SHI Xuemei. Membership clouds and membership cloud generators[J]. Journal of computer research & development, 1995, 32(6):15-20.
[22] 李德毅, 杜鹢. 不确定性人工智能[M]. 北京:国防工业出版社, 2014.
[23] YAO Yiyu. An outline of a theory of three-way decisions[C]//Proceedings of the 8th International RSCTC Conference. Chengdu, China:Springer, 2012, 7413:1-17.
[24] 姚一豫. 三支决策研究的若干问题[M]//刘盾, 李天瑞, 苗夺谦, 等. 三支决策与粒计算[M]. 北京:科学出版社, 2013:1-13.
[25] HU Baoqing. Three-way decisions space and three-way decisions[J]. Information sciences, 2014, 281:21-52.
[26] MA Lijia, GONG Maoguo, YAN Jianan et al. A decomposition-based multi-objective optimization for simultaneous balance computation and transformation in signed networks[J]. Information sciences, 2017, 378:144-160.
[27] GUERIN C. RIGAUD C. BERTET K. et al. An ontology-based framework for the automated analysis and interpretation of comic book’s images[J]. Information sciences. 2017, 378:109-130.
[28] YAO Yiyu, SHE Yanhong. Rough set models in multigranulation spaces[J]. Information sciences, 2016, 327:40-56.
[29] ZHANG Xiaohong, MIAO Duoqian, LIU Caihui, et al. Constructive methods of rough approximation operators and multigranulation rough sets[J]. Knowledge-based systems, 2016, 91:114-125.
[30] 苗夺谦, 徐菲菲, 姚一豫, 等. 粒计算的集合论描述[J]. 计算机学报, 2012, 35(2):351-363. MIAO Duoqian, XU Feifei, YAO Yiyu, et al. Set-theoretic formulation of granular computing[J]. Chinese journal of computers, 2012, 35(2):351-363.
[31] ZHANG Qinghua, WANG Guoyin. The uncertainty measure of hierarchical quotient space structure[J]. Mathematical problems in engineering, 2011, 2011, 513195.
[32] WANG Guoyin, XU Ji, ZHANG Qinghua, et al. Multi-granularity intelligent information processing[C]//Proceedings of the 15th International Conference, RSFDGrC. Tianjin, China:Springer, 2015:36-48.
[33] QIAN Yuhua, LIANG Jiye, Yao Yiyu, Dang Chuangyin. MGRS:A multi-granulation rough set[J]. Information sciences, 2010, 180:949-970.
[34] LIN Guoping, QIAN Yuhua, LI Jinjin. NMGRS:neighborhood-based multigranulation rough sets[J]. International journal of approximate reasoning, 2012, 53(7):1080-1093.
[35] XU Weihua, WANG Qiaorong, ZHANG Xiantao. Multi-granulation rough sets based on tolerance relations[J]. Soft computing, 2013, 17(7):1241-1252.
[36] Chen Yan. An adjustable multigranulation fuzzy rough set[J]. International journal of machine learning and cybernetics, 2016, 7(2):267-274.
[37] TAN Anhui, WU Weizhi, LI Jinjin, et al. Evidence-theory-based numerical characterization of multigranulation rough sets in incomplete information systems[J]. Fuzzy sets and systems, 254(2016):18-35.
[38] LI Feijiang, QIAN Yuhua, WANG Jieting et al. Multigranulation information fusion:a dempster-shafer evidence theory-based clustering ensemble method[J]. Information sciences, 2017, 378:389-409.
[39] WU Weizhi, LEUNG Y. Optimal scale selection for multi-scale decision tables[J]. International journal of approximate reasoning, 2013, 54(8):1107-1129.
[40] GU Shenming, WU Weizhi. On knowledge acquisition in multi-scale decision systems[J]. International journal of machine learning and cybernetics, 2013, 4(5):477-486.
[41] ZHU W. Topological approaches to covering rough sets[J]. Information sciences, 2007, 177(6):1499-1508.
[42] ZHU W, WANG Feiyue. On three types of covering-based rough sets[J]. IEEE transactions on knowledge and data engineering, 2007, 19(8):1131-1144.
[43] ZHU W. Relationship between generalized rough sets based on binary relation and covering[J]. Information sciences, 2009, 179(3):210-225.
[44] LIU Caihui, MIAO Duoqian, QIAN Jin. On multi-granulation covering rough sets[J]. International journal of approximate reasoning, 2014, 55(6):1404-1418.
[45] LIU Caihui, PEDRYCZ W. Covering-based multi-granulation fuzzy rough sets[J]. Journal of intelligent & fuzzy systems, 2016, 30(1):303-318.
[46] FENG Qinrong, MIAO Duoqian, CHENG Yi. Hierarchical decision rules mining[J]. Expert systems with applications, 2010, 37(3):2081-2091.
[47] QIAN Jin, LV Ping, YUE Xiaodong et al. Hierarchical attribute reduction algorithms for big data using MapReduce[J]. Knowledge-based systems. 2015, 73:18-31.
[48] CHEN Hongmei, LI Tianrui, RUAN Da. Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining[J]. Knowledge-based systems, 2012, 31:140-161.
[49] CHEN Hongmei, LI Tianrui, LUO Chuan, et al. A rough set-based method for updating decision rules on attribute values’ coarsening and refining[J]. IEEE transactions on knowledge and data engineering, 2014, 26(12):2886-2899.
[50] 张贤勇. 基于精度与程度逻辑组合的几类粗糙集模型及其算法研究[D]. 成都:四川师范大学, 2011. ZHANG Xianyong. Study on several rough set models and their algorithms based on logical combinations of precision and grade[D]. Chengdu:Sichuan Normal University, 2011.
[51] XU Weihua, GUO Yanting. Generalized multigranulation double-quantitative decision-theoretic rough set[J]. Knowledge-based systems, 2016, 105:190-205.
[52] FANG Bowen, HU Baoqing. Probabilistic graded rough set and double relative quantitative decision-theoretic rough set[J]. International journal of approximate reasoning, 2016, 74:1-12.
[53] ZHANG Xianyong, MIAO Duoqian. Double-quantitative fusion of accuracy and importance:systematic measure mining, benign integration construction, hierarchical attribute reduction[J]. Knowledge-based systems, 2016, 91:219-240.
[54] 李德毅, 刘常昱, 杜鹢, 等. 不确定性人工智能[J]. 软件学报, 2004, 15(11):1583-1594. LI Deyi, LIU Changyu, DU Yi, et al. Artificial intelligence with uncertainty[J]. Journal of software, 2004, 15(11):1583-1594.
[55] 王国胤, 张清华. 不同知识粒度下粗糙集的不确定性研究[J]. 计算机学报, 2008, 31(9):1588-1598. WANG Guoyin, ZHANG Qinghua. Uncertainty of rough sets in different knowledge granularities[J]. Chinese journal of computers, 2008, 31(9):1588-1598.
[56] 张清华, 王国胤, 胡军. 多粒度知识获取与不确定性度量[M]. 北京:科学出版社, 2013. ZHANG Qinghua, WANG Guoyin, HU Jun. Knowledge acquisition of multi-granular and uncertainty measurements[M]. Beijing:Science Press, 2013. (未找到本条文献英文信息,请核对)
[57] 刘宝碇, 彭锦. 不确定理论教程[M]. 北京:清华大学出版社, 2005. LIU Baoding, PENG Jin. Tutorial of uncertainty theory[M]. Beijing:Tsinghua Press, 2005.
[58] 苗夺谦, 李德毅, 姚一豫, 等. 不确定性与粒计算[M]. 北京:科学出版社, 2011. MIAO Duoqian, LI Deyi, YAO Yiyu, et al. Uncertainty and granular computing[M]. Beijing:Science Press, 2011.
[59] 苗夺谦, 王珏. 粗糙集理论中知识粗糙性与信息熵关系的讨论[J]. 模式识别与人工智能, 1998, 11(1):34-40. MIAO Duoqian, WANG Jue. On the relationships between information entropy and roughness of knowledge in rough set theory[J]. Pattern recognition & artificial intelligence, 1998, 11(1):34-40.
[60] DE LUCA A, TERMINI S. A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory[J]. Information and control, 1972, 20(4):301-312.
[61] 张文修, 吴伟志, 梁吉业, 等. 粗糙集理论与方法[M]. 北京:科学出版社, 2001. ZHANG Wenxiu, WU Weizhi, LIANG Jiye, et al. Rough sets theory and method[M]. Beijing:Science Press, 2001.
[62] LIANG Jiye, SHI Zhongzhi. The information entropy, rough entropy and knowledge granulation in rough set theory[J]. International journal of uncertainty, fuzziness and knowledge-based systems, 2004, 12(1):37-46.
[63] DEMPSTER A P. Upper and lower probabilities induced by a multivalued mapping[J]. The annals of mathematical statistics, 1967, 38(2):325-339.
[64] SHAFER G. A mathematical theory of evidence[M]. Princeton:Princeton University Press, 1976.
[65] 苗夺谦, 范世栋. 知识的粒度计算及其应用[J]. 系统工程理论与实践, 2002, 22(1):48-56. MIAO Duoqian, FAN Shidong. The calculation of knowledge granulation and its application[J]. Systems engineering-theory & practice, 2002, 22(1):48-56.
[66] 苗夺谦, 王国胤, 刘清等. 粒计算:过去、现在与展望[M]. 北京:科学出版社, 2007.
[67] QIAN Yuhua, LIANG Jiye. Combination entropy and combination granulation in rough set theory[J]. International journal of uncertainty, fuzziness and knowledge-based systems, 2008, 16(2):179-193.
[67] QIAN Yuhua, LIANG Jiye. Combination entropy and combination granulation in incomplete information system[J]. Lecture notes in computer science, 2006, 4062:184-190.
[69] WANG Junhong, LIANG Jiye, QIAN Yuhua, et al. Uncertainty measure of rough sets based on a knowledge granulation for incomplete information systems[J]. International journal of uncertainty, fuzziness and knowledge-based systems, 2008, 16(2):233-244.
[70] 梁吉业, 钱宇华. 信息系统中的信息粒与熵理论[J]. 中国科学E辑:信息科学, 2008, 38(12):2048-2065. LIANG Jiye, QIAN Yuhua. Information granules and entropy theory in information systems[J]. Science in China series F:information sciences, 2008, 51(10):1427-1444.
[71] BAYES T. An essay towards solving a problem in the doctrine of chances[J]. Philosophical transactions of the royal society, 53(1763):370-418.
[72] YU Jianhang, ZHANG Xiaoyan, ZHAO Zhenhua et al. Uncertainty measures in multigranulation with different grades rough set based on dominance relation[J]. Journal of intelligent & fuzzy systems, 2016,31:1133-1144.
[73] LIN Guoping, LIANG Jiye, QIAN Yuhua. Uncertainty measures for multigranulation approximation space[J]. International journal of uncertainty fuzziness and knowledge-based systems, 2015, 23(3) 443-457.
[74] ZHANG Qinghua, ZHANG Qiang, WANG Guoyin. The uncertainty of probabilistic rough sets in multi-granulation spaces[J]. International journal of approximate reasoning, 2016, 77:38-54.
[75] 苗夺谦. Rough Set理论及其在机器学习中的应用研究[D]. 1997, 北京,中国科学院自动化研究所.MIAO Duoqian. Rough set theory and its application in machine learning[D]. 1997, Beijing, Institute of Automation, Chinese Academy of Sciences.
[76] MA Zhouming, MI Jusheng. A comparative study of MGRSs and their uncertainty measures[J]. Fundamenta informaticae, 2015, 142:161-181.
[77] HUANG Bing, GUO Chunxiang, LI Huaxiong, et al. Hierarhical structures and uncertainty measures for intuitionistic fuzzy approximate space[J]. Information Sciences, 2016, 336:92-114.
[78] LIANG Jiye, WANG Feng, DANG Chuangyin, et al. An efficient rough feature selection algorithm with a multi-granulation view[J]. International journal of approximate reasoning, 2012, 53(6):912-926.
[79] LIN Yaojin, LI Jinjin, LI Peirong, et al. Feature selection via neighborhood multi-granulation fusion[J]. Knowledge-based systems, 2014, 67:162-168.
[80] 桑妍丽,钱宇华. 一种悲观多粒度粗糙集中的粒度约简算法[J]. 模式识别与人工智能, 2012, 25(3):361-366. SANG Yanli, QIAN Yuhua. A granular space reduction approach to pessimistic multi-granulation rough sets[J]. Pattern recognition and artificial intelligence, 2012, 25(3):361-366.
[81] SHE Yanhong, LI Jinhai, YANG Hailong. A local approach to rule induction in multi-scale decision tables[J]. Knowledge-based systems, 2015, 89:398-410.
[82] 邓大勇, 徐小玉, 黄厚宽. 基于并行约简的概念漂移探测[J]. 计算机研究与发展, 2015, 52(5):1071-1079.DENG Dayong, XU Xiaoyu, HUANG Houkuan. Concept drifting detection for categorical evolving data based on parallel reducts[J]. Journal of computer research and development, 2015, 52(5):1071-1079.
[83] 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.
[84] RUAN Junhu, WANG Xuping, SHI Yan. Developing fast predictors for large-scale time series using fuzzy granular support vector machines[J]. Applied soft computing, 2013, 13(6):3981-4000.
[85] HE Jieyue, ZHONG Wei, HARRISON R, et al. Clustering support vector machines and its application to local protein tertiary structure prediction[C]//Proceedings of the 6th International Conference on Computational Science. Reading, UK:Springer-Verlag, 2006:710-717.
[86] 欧阳继红, 刘燕辉, 李熙铭, 等. 基于LDA的多粒度主题情感混合模型[J]. 电子学报, 2015, 43(9):1875-1880. OUYANG Jihong, LIU Yanhui, LI Ximing, et al. Multi-grain sentiment/topic model based on LDA[J]. Acta electronica sinica, 2015, 43(9):1875-1880.
[87] WANG Xuekuan, ZHAO Cairong, MIAO Duoqian, et al. Fusion of multiple channel features for person re-identification[J]. Neurocomputing, 2016, 213:125-136.
[88] XU Weihua, YU Jianhang. A novel approach to information fusion in multi-source datasets:a granular computing viewpoint[J]. Information sciences, 2017, 378:410-423.
[89] LIN Guoping, LIANG Jiye, QIAN Yuhua. et al. A fuzzy multigranulation decision-theoretic approach to multisource fuzzy information systems[J]. Knowledge-based systems, 2016, 91:102-113.
[90] http://cncc.ccf.org.cn/struct/7.
[91] LI Jinhai, HUANG Chenchen, QI Jianjun et al. Three-way cognitive concept learning via multi-granularity[J]. Information Sciences. 2017, 378244-263.
[92] YAO Yiyu. Three-way decisions and cognitive computing[J]. Cognitive Computation. 2016, 8(4):543-554.
[93] 刘清, 邱桃荣,刘斓. 基于非标准分析的粒计算研究[J]. 计算机学报. 2015, 38(8):1618-1627.LIU Qing, QIU Taorong, LIU Lan. The research of granular computing based on nonstandard analysis[J]. Chinese journal of computers, 2015, 38(8):1618-1627.

相似文献/References:

[1]李德毅.网络时代人工智能研究与发展[J].智能系统学报,2009,4(01):1.
 LI De-yi.AI research and development in the network age[J].CAAI Transactions on Intelligent Systems,2009,4(6):1.
[2]赵克勤.二元联系数A+Bi的理论基础与基本算法及在人工智能中的应用[J].智能系统学报,2008,3(06):476.
 ZHAO Ke-qin.The theoretical basis and basic algorithm of binary connection A+Bi and its application in AI[J].CAAI Transactions on Intelligent Systems,2008,3(6):476.
[3]徐玉如,庞永杰,甘 永,等.智能水下机器人技术展望[J].智能系统学报,2006,1(01):9.
 XU Yu-ru,PANG Yong-jie,GAN Yong,et al.AUV—state-of-the-art and prospect[J].CAAI Transactions on Intelligent Systems,2006,1(6):9.
[4]王志良.人工心理与人工情感[J].智能系统学报,2006,1(01):38.
 WANG Zhi-liang.Artificial psychology and artificial emotion[J].CAAI Transactions on Intelligent Systems,2006,1(6):38.
[5]赵克勤.集对分析的不确定性系统理论在AI中的应用[J].智能系统学报,2006,1(02):16.
 ZHAO Ke-qin.The application of uncertainty systems theory of set pair analysis (SPU)in the artificial intelligence[J].CAAI Transactions on Intelligent Systems,2006,1(6):16.
[6]秦裕林,朱新民,朱 丹.Herbert Simon在最后几年里的两个研究方向[J].智能系统学报,2006,1(02):11.
 QIN Yu-lin,ZHU Xin-min,ZHU Dan.Herbert Simons two research directions in his lost years[J].CAAI Transactions on Intelligent Systems,2006,1(6):11.
[7]谷文祥,李 丽,李丹丹.规划识别的研究及其应用[J].智能系统学报,2007,2(01):1.
 GU Wen-xiang,LI Li,LI Dan-dan.Research and application of plan recognition[J].CAAI Transactions on Intelligent Systems,2007,2(6):1.
[8]杨春燕,蔡 文.可拓信息-知识-智能形式化体系研究[J].智能系统学报,2007,2(03):8.
 YANG Chun-yan,CAI Wen.A formalized system of extension information-knowledge-intelligence[J].CAAI Transactions on Intelligent Systems,2007,2(6):8.
[9]赵克勤.SPA的同异反系统理论在人工智能研究中的应用[J].智能系统学报,2007,2(05):20.
 ZHAO Ke-qin.The application of SPAbased identicaldiscrepancycontrary system theory in artificial intelligence research[J].CAAI Transactions on Intelligent Systems,2007,2(6):20.
[10]王志良,杨 溢,杨 扬,等.一种周期时变马尔可夫室内位置预测模型[J].智能系统学报,2009,4(06):521.[doi:10.3969/j.issn.1673-4785.2009.06.009]
 WANG Zhi-liang,YANG Yi,YANG Yang,et al.A periodic time-varying Markov model for indoor location prediction[J].CAAI Transactions on Intelligent Systems,2009,4(6):521.[doi:10.3969/j.issn.1673-4785.2009.06.009]
[11]马世龙,乌尼日其其格,李小平.大数据与深度学习综述[J].智能系统学报,2016,11(6):728.[doi:10.11992/tis.201611021]
 MA Shilong,WUNIRI Qiqige,LI Xiaoping.Deep learning with big data: state of the art and development[J].CAAI Transactions on Intelligent Systems,2016,11(6):728.[doi:10.11992/tis.201611021]

备注/Memo

备注/Memo:
收稿日期:2016-12-13。
基金项目:国家自然科学基金项目(61573255,61673301,61472056,61432011,61572091,61573321,61272021,U1435212,41631179).
作者简介:苗夺谦,男,1964年生,教授,博士生导师,博士,主要研究方向为机器学习、粒计算、人工智能、大数据分析。国际粗糙集学会指导委员会主席,中国人工智能学会常务理事,粗糙集与软计算专委会主任、中国计算机学会杰出会员,人工智能与模式识别专委会委员,上海市计算机学会常务理事,同济大学嵌入式系统与服务计算教育部重点实验室副主任,发表学术论文多篇;张清华,男,1974年生,教授,博士生导师,博士,主要研究方向为粗糙集,粒计算,不确定人工智能;钱宇华,男,1976年生,教授,博士生导师,博士,主要研究方向为人工智能、数据挖掘与机器学习等。
通讯作者:苗夺谦.E-mail:dqmiao@tongji.edu.cn.
更新日期/Last Update: 1900-01-01