计算机科学 ›› 2018, Vol. 45 ›› Issue (9): 1-10.doi: 10.11896/j.issn.1002-137X.2018.09.001
• 综述 • 下一篇
姚艳玲
YAO Yan-ling
摘要: 文献共被引可以为目标领域研究前沿的分析研究提供一种更加客观、全面的研究视角。文中利用文献共被引分析对2017年国际上人工智能领域的131篇ESI高被引论文进行分析,探寻得到了2017年该领域中包含的12个研究前沿和2个重点研究前沿。通过对研究前沿中核心论文的进一步研究发现,在2017年国际人工智能领域的多个研究前沿中,我国的学者已经成长为中坚力量,发挥着重要的作用。相比而言,在深度学习的两个重点研究前沿中,我国还缺乏高质量核心论文的产出者,这也激励着我国学者不断为之努力。
中图分类号:
[1]CLARIVATE.Web of Science在线大讲堂[EB/OL].[2017-12-30].https://clarivate.com.cn/e-clarivate/wos_research_0926.htm. [2]YANG X,LIN T Y,YANG J,et al.Combination of interval-va-lued fuzzy set and soft set[J].Computers & Mathematics with Applications,2009,58(3):521-527. [3]FENG F,LI C,DAVVAZ B,et al.Soft sets combined with fuzzy sets and rough sets:a tentative approach[J].Soft Computing,2010,14(9):899-911. [4]FENG F,LI Y,LEOREANU-FOTEA V.Application of level soft sets in decision making based on interval-valued fuzzy soft sets[J].Computers & Mathematics with Applications,2010,60(6):1756-1767. [5]KONG Z,ZHANG G,WANG L,et al.An efficient decision making approach in incomplete soft set[J].Applied Mathematical Modelling,2014,38(7/8):2141-2150. [6]MENG D,ZHANG X,QIN K.Soft rough fuzzy sets and soft fuzzy rough sets[J].Computers & Mathematics with Applications,2011,62(12):4635-4645. [7]WANG J Q,LI K J,ZHANG H Y.Interval-valued intuitionistic fuzzy multi-criteria decision-making approach based on prospect score function[J].Knowledge-Based Systems,2012,27(3):119-125. [8]SUN B,MA W.Soft fuzzy rough sets and its application in decision making[J].Artificial Intelligence Review,2014,41(1):67-80. [9]SHABIR M,IRFAN ALI M,SHAHEEN T.Another approach to soft rough sets[J].Knowledge-Based Systems,2013,40(1):72-80. [10]YAO Y,DENG X.Quantitative rough sets based on subsethood measures[J].Information Sciences,2014,267(5):306-322. [11]ÇAGMAN N,ENGINOGLU S.Soft set theory and uni-int decision making[J].European Journal of Operational Research,2010,207(2):848-855. [12]LI Z,XIE T.The relationship among soft sets,soft rough sets and topologies[J].Soft Computing,2014,18(4):717-728. [13]TIAN Z P,WANG J,WANG J Q,et al.Simplified Neutrosophic Linguistic Multi-criteria Group Decision-Making Approach to Green Product Development[J].Group Decision & Negotiation,2017,26(3):1-31. [14]XIA M,XU Z,ZHU B.Some issues on intuitionistic fuzzy aggregation operators based on Archimedean t-conorm and t-norm[J].Knowledge-Based Systems,2012,31(7):78-88. [15]LIN R,ZHAO X F,WEI G W.Models for selecting an ERP system with hesitant fuzzy linguistic information[J].Journal of Intelligent & Fuzzy Systems,2014,26(5):2155-2165. [16]MERIGÓ J M,CASANOVAS M.Induced Aggregation Operators in Decision Making with the Dempster-Shafer Belief Structure[J].International Journal of Intelligent Systems,2009,24(8):7138-7149. [17]LIU Z,BLASCH E,XUE Z,et al.Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision:A Comparative Study[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,34(1):94-109. [18]FAUVEL M,TARABALKA Y,BENEDIKTSSON J A,et al. Advances in Spectral-Spatial Classification of Hyperspectral Ima-ges[J].Proceedings of the IEEE,2013,101(3):652-675. [19]LIU Y,LIU S,WANG Z.A general framework for image fusion based on multi-scale transform and sparse representation[J].Information Fusion,2015,24(4):147-164. [20]LI S,KANG X,HU J.Image Fusion with Guided Filtering[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2013,22(7):2864. [21]YANG C,ZHANG J Q,WANG X R,et al.A novel similarity based quality metric for image fusion[J].Information Fusion,2008,9(2):156-160. [22]BAI X,ZHANG Y,ZHOU F,et al.Quadtree-based multi-focus image fusion using a weighted focus-measure[J].Information Fusion,2015,22(2):105-118. [23]HOSSNY M,NAHAVANDI S,CREIGHTON D,et al.Image fusion performance metric based on mutual information and entropy driven quadtree decomposition[J].Electronics Letters,2010,46(18):1266-1268. [24]ZHOU Z,LI S,WANG B.Multi-scale weighted gradient-based fusion for multi-focus images[J].Information Fusion,2014,20(1):60-72. [25]JIANG Y,WANG M.Image fusion with morphological component analysis[J].Information Fusion,2014,18(1):107-118. [26]DE I,CHANDA B.Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure[J].Information Fusion,2013,14(2):136-146. [27]DONG C,LOY C C,HE K,et al.Image Super-Resolution Using Deep Convolutional Networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2016,38(2):295-307. [28]ZHOU Q,SHI P,LIU H,et al.Neural-Network-Based Decentralized Adaptive Output-Feedback Control for Large-Scale Stochastic Nonlinear Systems[J].IEEE Transactions on Systems Man & Cybernetics Part B,2012,42(6):1608-1619. [29]CHEN B,LIU X P,GE S S,et al.Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach[J].IEEE Transactions on Fuzzy Systems,2012,20(6):1012-1021. [30]WANG H,LIU K,LIU X,et al.Neural-Based Adaptive Output-Feedback Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems[J].IEEE Transactions on Cybernetics,2015,45(9):1977. [31]LIU Y J,TONG S.Barrier Lyapunov Functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints[J].Automatica,2016,64(C):70-75. [32]XIE X,YUE D,ZHANG H,et al.Control Synthesis of Discrete-Time T-S Fuzzy Systems via a Multi-Instant Homogenous Polynomial Approach[J].IEEE Transactions on Cybernetics,2016,46(3):630-640. [33]QIU J,GAO H,DING S X.Recent Advances on Fuzzy-Model-Based Nonlinear Networked Control Systems:A Survey[J].IEEE Transactions on Industrial Electronics,2016,63(2):1207-1217. [34]LIU Z,WANG F,ZHANG Y,et al.Adaptive Fuzzy Output-Feedback Controller Design for Nonlinear Systems via Backstepping and Small-Gain Approach[J].IEEE Transactions on Cybernetics,2014,44(10):1714-1725. [35]LONG F,FEI S.Neural networks stabilization and disturbance attenuation for nonlinear switched impulsive systems[J].Neurocomputing,2008,71(7-9):1741-1747. [36]LONG L,ZHAO J.Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time[J].IEEE Transactions on Neural Networks & Learning Systems,2015,26(7):1350. [37]JIANG W,WEI B,ZHAN J,et al.A visibility graph power ave-raging aggregation operator:A methodology based on network analysis[J].Computers & Industrial Engineering,2016,101(11):260-268. [38]LIU H C,YOU J X,FAN X J,et al.Failure mode and effects analysis using D numbers and grey relational projection method[J].Expert Systems with Applications,2014,41(10):4670-4679. [39]SU X,MAHADEVAN S,HAN W,et al.Combining dependent bodies of evidence[J].Applied Intelligence,2016,44(3):634-644. [40]MARDANI A,JUSOH A,ZAVADSKAS E K.Fuzzy multiple criteria decision-making techniques and applications-Twodeca-des review from 1994 to 2014[J].Expert Systems with Applications,2015,42(8):4126-4148. [41]LU J,ZHONG J,TANG Y,et al.Synchronization in output-coupled temporal Boolean networks[J].Sci Rep,2014,4(Step5):6292. [42]LIANG J,WANG Z,LIU Y,et al.State estimation for two-dimensional complex networks with randomly occurring nonli-nearities and randomly varying sensor delays[J].International Journal of Robust & Nonlinear Control,2014,24(1):18-38. [43]WANG Z,SHEN B,SHU H,et al.Quantized H-infinity Control for Nonlinear Stochastic Time-Delay Systems With Missing Measurements[J].IEEE Transactions on Automatic Control,2012,57(6):1431-1444. [44]ZHANG H,QIN C,LUO Y.Neural-Network-Based Constrai-ned Optimal Control Scheme for Discrete-Time Switched Nonli-near System Using Dual Heuristic Programming[J].IEEE Transactions on Automation Science & Engineering,2014,11(3):839-849. [45]LIU D,WEI Q.Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems[J].IEEE Trans Neural Netw Learn Syst,2014,25(3):621-634. [46]BAO H B,CAO J D.Projective synchronization of fractional-order memristor-based neural networks[J].Neural Netw,2015,63(3):1-9. [47]DONG Y,LI C C,XU Y,et al.Consensus-Based Group Decision Making Under Multi-granular Unbalanced 2-Tuple Linguistic Preference Relations[J].Group Decision & Negotiation,2015,24(2):217-242. [48]DONG Y,ZHANG G,HONG W C,et al.Linguistic Computational Model Based on 2-Tuples and Intervals[J].IEEE Transactions on Fuzzy Systems,2013,21(6):1006-1018. [49]DONG Y,HERRERA-VIEDMA E.Consistency-Driven Auto-matic Methodology to Set Interval Numerical Scales of 2-Tuple Linguistic Term Sets and Its Use in the Linguistic GDM With Preference Relation[J].IEEE Transactions on Cybernetics,2017,45(4):780-792. [50]HERRERA F,HERRERA-VIEDMA E,MARTINEZ L.A fuzzy linguistic methodology to deal with unbalanced linguistic term sets[J].IEEE Transactions on Fuzzy Systems,2008,16(2):354-370. [51]WANG J Q,WANG D D,ZHANG H Y,et al.Multi-criteria group decision making method based on interval 2-tuple linguistic information and Choquet integral aggregation operators[J].Soft Computing,2015,19(2):389-405. [52]DENG J,DONG W,SOCHER R,et al.ImageNet:A Large-Scale Hierarchical Image Database[C]∥IEEE Conference on Computer Vision & Pattern Recognition.2009:248-255. [53]CHEN Y,LIN Z,ZHAO X,et al.Deep Learning-Based Classification of Hyperspectral Data[J].IEEE Journal of Selected To-pics in Applied Earth Observations & Remote Sensing,2017,7(6):2094-2107. [54]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥IEEE Conference on Computer Vision & Pattern Recognition(CVPR).2014:580-587. [55]BENGIO Y,COURVILLE A,VINCENT P.Representation Lear-ning:A Review and New Perspectives[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2013,35(8):1798-1828. [56]FELZENSZWALB P F,GIRSHICK R B,MCALLESTER D,et al.Object Detection with Discriminatively Trained Part-Based Models[J].IEEE Trans Pattern Anal Mach Intell,2010,32(9):1627-1645. [57]SU H,RONG Z,CHEN M Z Q,et al.Decentralized Adaptive Pinning Control for Cluster Synchronization of Complex Dyna-mical Networks[J].IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society,2013,43(1):394-399. [58]WEN G,DUAN Z,CHEN G,et al.Consensus Tracking of Multi-Agent Systems With Lipschitz-Type Node Dynamics and Switching Topologies[J].IEEE Transactions on Circuits & Systems I Regular Papers,2014,61(2):499-511. [59]SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:A Simple Way to Prevent Neural Networks from Overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958. [60]LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436. [61]JARRETT K,KAVUKCUOGLU K,RANZATO M,et al.What is the Best Multi-Stage Architecture for Object Recognition?[J].IEEE International Conference on Computer Vision,2010,30(2):2146-2153. [62]AHN C K,WU L,SHI P.Stochastic stability analysis for 2-D Roesser systems with multiplicative noise[J].Automatica,2016,69(7):356-363. [63]BOULKROUNE A,BOUZERIBA A,HAMEL S,et al.A projective synchronization scheme based on fuzzy adaptive control for unknown multivariable chaotic systems[J].Nonlinear Dynamics,2014,78(1):433-447. [64]CHEN M,GE S S,et al.Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities[J].IEEE Transactions on Neural Networks,2010,21(5):796. [65]HAN T T,GE S S,TONG H L.Adaptive neural control for a class of switched nonlinear systems[J].Systems & Control Letters,2009,58(2):109-118. |
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