个人简历

工作履历
2007年4月至今 北京科技大学自动化学院 控制科学与工程系任教
教育背景
2003年9月至2007年4月 北京科技大学信息工程学院 博士研究生
2013年2月至2014年2月 美国佐治亚理工学院电子与计算机工程系 访问学者

研究领域

数据挖掘
机器学习
复杂系统建模"代表性项目
国家863项目“冶金工业MES架构和关键技术研究及示范应用”
国家科技部攻关项目“工业过程建模平台”
国家科技部攻关项目“国家材料自然环境腐蚀数据挖掘方法研究及软件开发”
中央高校基本科研业务费学科发展科研基金项目“基于数据驱动的热轧带钢力学性能预报过程建模及优化研究”
国家自然基金项目“基于增量学习的演化模糊系统的解释性问题研究”"

近期论文

代表性论著
教材：
王玲, 数据挖掘学习方法[D], 冶金工业出版社, 2017.7
论文：
[1] Ling Wang, Dong Mei Fu. Efficient Support Vector Regression with Weighted Constraints, 2011 3rd International Asia Conference on Informatics in Control, Automation and Robotics, 2011，2, 607-615
[2] Wang Ling, Fu DongMei, Li Qing. Samples Selection Based on SVR for Prediction of Steel Mechanical Property, 2012 International Conference on Intelligent Systems Design and Engineering Applications, 2012: 909-912.
[3] 王玲，付冬梅，李擎. 基于KFA-KPLS的钢材力学性能预报模型, 第31届中国控制会议, 2012，7005-7008
[4] Ling wang, Dong-Mei Fu, Wei-Dong Yang, A multiple SVR modeling of hot rolling process combined with kernel clustering and grey relational grade, proceedings of the 2012 International Conference on Machine Learning and Cybernetics, 2012, 1:387-393
[5] Wang Ling，Guo Hui，Fu Dong-Mei，Modeling for prediction steel harden-ability based on IGA-KPLS，2010 29th Chinese Control Conference (CCC)，北京，2010.07.5061- 5065
[6] WANG L, ZHANG D, WU L. Fuzzy Model Based on Multi-level Fuzzy Information Granulation for Regression Estimation[J]. Journal of Computational Information Systems, 2013, 9(11): 4433-4441.
[7] WANG L, WU L. A new Incremental Learning method based Support Vector Regression for system modeling[C], 2013 32st Chinese. IEEE Control Conference (CCC), 2013:1900-1904.
[8] Qing Li, Ling Wang, Zheng Zhang De, Cun Zhang Wei. Decremental learning based on sample-weighted support vector regression, Proceedings of the 2012 24th Chinese Control and Decision Conference, CCDC 2012, 2012：1322-1325
[9] Wang Ling，Guo Hui，Fu Dong-Mei，Modeling for prediction steel harden-ability based on IGA-KPLS，2010 29th Chinese Control Conference (CCC)，北京，2010.07.5061- 5065
[10]Wang Ling, Zhang De Zheng, Wu Lu Lu, fuzzy model based on Multi-level fuzzy information granulation for regression estimation(2013.06.01). Journal of Computational Information Systems, 2013,9(11):4433-4441.
[11]王玲, 吴璐璐,付冬梅. 一种基于密度的模糊自适应聚类算法[J](2014.11.01). 北京科技大学学报, 2014,11(36):1560-1565.
[12]Wang Ling, Wu Lulu, Adaptive learning by using a new evolving clustering method(2014.11.01). Journal of Computational Information System, 2014,21(10):9461-9468. [13]Wang Ling, Guo Hui, Feature selection based on fuzzy clustering analysis and association rule mining for soft-sensor, Proceedings of the 33rd Chinese Control Conference(CCC), July 28-30, 2014, Nanjing, China,5162-5166.
[14] Wang Ling, Wu Lulu, Fuzzy rules extraction based on output-interval clustering and support vector regression for forecasting. Journal of Intelligent & Fuzzy Systems, 2014, 27(5): 2563–2571.
[15] Ling Wang, Dong Mei Fu, Lu Lu wu, Incremental Hierarchical Fuzzy model generated from Multilevel Fuzzy Support Vector Regression Network. Informatic, 2014, 38: 367-376.
[16]Wang Ling,Wu Lu Lu. A new Incremental Learning method based Support Vector Regression for system modeling[C], 2013 32st Chinese. IEEE Control Conference (CCC), 2013:1900-1904.
[17]Ling Wang, Ji-Yuan Dong, Shu-Lin Li，Fuzzy Inference Algorithm Based on Quantitative Association Rules. Procedia Computer Science, Volume 61, 2015, 388–394.
[18] 王玲,孙华,基于自适应学习的演化聚类算法, 控制与决策, 2016,32(3):423-428, EI检索号: 20161502211146
[19] 王玲，李树林，吴璐璐, 基于定量关联规则树的分类及回归预测算法, 工程科学学报, 2016,38 (6 ): 886-892
[20] Wang L, Li S L, Sun H, et al. A classification and regression algorithm based on quantitative association rule tree[J]. Journal of Intelligent & Fuzzy Systems, 2016, 31(3):1407-1418.
[21]Wang L, Xiao X, Meng J. Prediction of air pollution based on FCM-HMM Multi-model[C]//Control Conference (CCC), 2016 35th Chinese. TCCT, 2016: 2057-2062.