杨隆浩-凯发k8登录

杨隆浩，博士，博士生导师，奥斯特大学（英国）联合博士生导师，福州大学教授（校聘），旗山学者，福建省高层次现有人才，香港理工大学博士后，奥斯特大学（英国）博士后，哈恩大学（西班牙）访问学者，电气电子工程师学会（美国）成员，自动化学会会员，智能推理与决策专委会委员；长期从事证据推理、置信规则库推理、环境治理成本预测等领域研究；主持国家自科基金项目、教育部人文社科项目、福建省社科规划项目、福建省自科基金项目、福州大学科研启动项目和福州大学研究生教改项目，以及作为主要成员参与10余项国家级和省部级项目；发表近80篇国内外学术论文，其中sci/ssci收录期刊论文50余篇。获邀担任《系统工程理论与实践》《控制与决策》和asoc、eaai、eswa、ija、ins、inffus、ieee-tfs、ieee-tsmc、ieee-tcyb、jclp、jema、jress、kbs等30余个国内外期刊的同行评议专家。

博士招生：管理系统工程（学术型）（有读博潜力的学生可提供研究助理岗位，待遇可谈）
硕士招生：管理科学与工程（学术型）、信息管理与信息系统（学术型）、工业工程与管理（专业型）。
招生要求：有拿全日制研究生国家奖学金的想法，且能为此想法付出行动。
学生福利：收获导师兼朋友，提供助研奖学金和科研奖励，可提前攻博、海外访学和推荐海外攻博（含奖学金）。
团队成果：idea team团队ag凯发旗舰厅主页（）、idea team算法平台（，仅福大校内网可访问）

学习经历
2015 - 2019，福州大学经济与管理学院，博士
2012 - 2015，福州大学数学与计算机科学学院，硕士
2008 - 2012，福州大学数学与计算机科学学院，学士

工作经历
2025 - 至今，福州大学经济与管理学院，教授（校聘）
2022 - 2024，福州大学经济与管理学院，副教授
2019 - 2021，福州大学经济与管理学院，讲师
2019 - 2020，英国奥斯特大学计算机学院，博士后
2017 - 2018，西班牙哈恩大学计算机学院，访问学者

研究方向
理论方面：证据推理、数据驱动建模、置信规则库推理、可解释性的机器学习
应用方面：环境治理成本预测、智能家居中活动识别、双碳目标下碳达峰预测、非均衡数据下临床诊断

科研项目
国家自科面上项目，深度置信规则库的自适应建模方法及在智能家居活动识别中的应用，2025-2028，主持，在研
国家自科青年项目，置信规则库推理模型的集成式动态建模方法及应用研究，2021-2023，主持，结题
教育部社科规划项目，预测视角下兼顾目标分解和准则设计的我国省域碳配额分配研究，2024-2027，主持，在研
教育部社科青年项目，数据驱动下基于指标设计和效率测度的环境治理成本预测方法研究，2020-2022，主持，结题
福建省教改项目，“竞赛 科研”双驱动下管理类专业高校拔尖创新人才培养路径探索，2024-2025，主持，在研
福建省社会科学规划项目，“双碳”目标下基于集成预测的我国省域碳配额分配研究，2024-2027，主持，在研
福建省社会科学规划项目，基于置信规则库参数和结构学习的大气污染治理成本预测研究，2019-2022，主持，结
福建省自然科学基金项目，基于聚类分析的置信规则库建模新方法及应用研究，2020-2023，主持，结题
福州大学科研启动项目，置信规则库推理模型的最优决策结构研究，2021-2023，主持，结题
福州大学教改项目，疫情防控常态化下经管类研究生培养质量影响因素及提升途径研究，2021-2023，主持，结题

论著情况

◆ 一作论文

1. forecasting carbon peaking in china using data-driven rule-base model: an in-depth analysis across regional and economic scenarios[j]. journal of cleaner production, 2024, 451: 142053. (sci; if 10.2, 中科院1区&学科top期刊)

2. a data-driven rule-base approach for carbon emission trend forecast with environmental regulation and efficiency improvement[j]. sustainable production and consumption, 2024, 45: 316-332. (ssci & sci; if 10.3,中科院1区&学科top期刊)

3. 基于规则聚类和参数学习的扩展置信规则库推理模型[j]. 控制与决策, 2024, 39(8): 2685-2693. (ei)

4. extended belief rule base with ensemble imbalanced learning for lymph node metastasis diagnosis in endometrial carcinoma[j]. engineering applications of artificial intelligence, 2023, 126: 106950. (sci; if 7.5, 中科院1区&学科top期刊)

5. an ensemble model for efficiency evaluation of enterprise performance based on evidential reasoning approach[j]. journal of intelligent & fuzzy systems, 2023, 45(2): 2477-2495. (sci; if 1.7, 中科院分区4区)

6. cumulative belief rule-based expert system for multi-resident activity recognition in smart home[c]. the 2023 ieee international conference on intelligent systems and knowledge engineering (iske2023), 2023, november 17-19, fuzhou, china. (ei)

7. predicting remaining useful life of lithium-ion battery using extended belief rule base model [c]. the 2023 ieee international conference on intelligent systems and knowledge engineering (iske2023), 2023, november 17-19, fuzhou, china. (ei)

8. extended belief rule-based system using bi-level joint optimization for environmental investment forecasting[j]. applied soft computing, 2023, 140: 110275. (sci, if: 8.263, 中科院1区 & 科学top期刊)

9. belief rule-base expert system with multilayer tree structure for complex problems modeling[j]. expert system with applications, 2023, 217: 119567. (sci & ei, if: 8.665, 中科院1区 & 科学top期刊)

10. 基于聚类集成和激活因子的扩展置信规则库推理模型[j]. 控制与决策, 2023, 38(3): 815-824. (ei)

11. an ensemble extended belief rule base decision model for imbalanced classification problems[j]. knowledge-based systems, 2022, 242: 108410. (sci, if: 8.038, 中科院1区 & 科学top期刊)

12. highly explainable cumulative belief rule-based system with effective rule-base modeling and inference scheme[j]. knowledge-based systems, 2022, 240: 107805. (sci, if: 8.038, 中科院1区 & 科学top期刊)

13. enhancing extended belief rule-based systems for classification problems based on decomposition strategy and overlap function[j]. international journal of machine learning and cybernetics, 2022, 12: 811-838. (sci, if: 4.012, 中科院3区)

14. research and development talents training in china universities - based on the consideration of education management cost planning [j]. sustainability, 2021, 13(17): 1-17. (sci & ssci, if: 3.251, 中科院4区)

15. improving micro-extended belief rule-based system using activation factor for classification problems[c]. the 6th international conference on belief functions (belief2021), 2021, oct. 15-19, shanghai, china. (ei)

16. online updating extended belief rule-based system for sensor - based activity recognition[j]. expert systems with applications, 2021, 186: 115737. (sci, if: 6.954, 中科院1区 & 学科top期刊)

17. an improved fuzzy rule-based system using evidential reasoning and subtractive clustering for environmental investment prediction[j]. fuzzy sets and systems, 2021, 421: 44-61. (sci & ssci, if: 3.343, 中科院1区 & 学科top期刊)

18. environmental investment prediction using extended belief rule - based system and evidential reasoning rule [j]. journal of cleaner production, 2021, 289: 125661. (sci & ssci, if: 9.297, 中科院1区 & 学科top期刊)

19. a micro-extended belief rule-based system for big data multi-class classification problems[j]. ieee transactions on systems, man, and cybernetics: systems, 2021, 51(1): 420-440. (sci, if: 13.451, 中科院1区 & 学科top期刊)

20. 基于扩展置信规则库联合优化的桥梁风险评估[j]. 系统工程理论与实践, 2020, 49(7): 1870-1881. (ei & cssci, 国家自然科学基金管理学报a类期刊)

21. ensemble belief rule base modeling with diverse attribute selection and cautious conjunctive rule for classification problems[j]. expert systems with applications, 2020, 146: 113161. (sci, if: 6.954, 中科院1区 & 学科top期刊)

22. new activation weight calculation and parameter optimization for extended belief rule-based system based on sensitivity analysis[j]. knowledge and information systems, 2019, 60: 837-878. (sci, if: 2.936, 中科院3区)

23. extended belief-rule-based system with new activation rule determination and weight calculation for classification problems[j]. applied soft computing, 2018, 72: 261-272. (sci, if: 5.472, 中科院1区 & 学科top期刊)

24. comparative analysis on extended belief rule-based system for activity recognition[c]. conference on data science and knowledge engineering for sensing decision support (flins 2018), 2018, august 21-24, belfast, northern ireland, uk.

25. a consistency analysis-based rule activation method for extended belief rule base system[j]. information sciences, 2018, 445-446: 50-65. (sci, if: 5.910, 中科院1区 & 学科top期刊)

26. a joint optimization method on parameter and structure for belief-rule- based systems[j]. knowledge-based systems, 2018, 142: 220-240. (sci, if: 5.921, 中科院1区 & 学科top期刊)

27. a disjunctive belief rule-based expert system for bridge risk assessment with dynamic parameter optimization model[j]. computers & industrial engineering, 2017, 113: 459-474. (sci, if: 3.195, 中科院2区)

28. a data envelopment analysis (dea)-based method for rule reduction in extended belief-rule-based systems[j]. knowledge-based systems, 2017, 123: 174-187. (sci, if: 5.921, 中科院1区 & 科学top期刊)

29. multi-attribute search framework for optimizing extended belief rule-based systems[j]. information sciences, 2016, 370-371: 159-183. (sci, if: 5.910, 中科院1区 & 学科top期刊)

30. 基于关联系数标准差融合的置信规则库规则约简方法[j]. 信息与控制, 2015, 44(1): 21-28, 37. (cscd)

31. 置信规则库参数学习的并行差分进化算法[j]. 山东大学学报(工学版), 2015, 45(1): 30-36.

32. 出租车乘车概率预测的置信规则库推理方法[j]. 计算机科学与探索, 2015, 9(8): 985-994. (cscd)

33. 面向最佳决策结构的置信规则库结构学习方法[j]. 计算机科学与探索, 2014, 8(10): 1216-1230. (cscd)

◆ 合作论文

34. micro-extended belief rule-based system with activation factor and parameter optimization for industrial cost prediction[j]. international journal of machine learning and cybernetics, 2023, 14: 63-78. (sci)

35. a novel data-driven decision model based on extended belief rule base to predict china’s carbon emissions[j]. journal of environmental management, 2022, 318: 115547. (sci)

36. a heterogeneous multi-attribute case retrieval method for emergency decision making based on bidirectional projection and todim[j]. expert systems with applications, 2022, 203: 117382. (sci)

37. dynamic rule activation method based on activation factor for extended belief rule - based systems[c]. the 16th international conference on intelligent systems and knowledge engineering (iske2021), 2021, nov. 26-28, chengdu, china. (ei)

38. 基于不同联合学习方法的扩展置信规则库环境治理成本预测[j]. 系统科学与数学, 2021, 41(3): 705-729. (cscd)

39. 大气污染治理效率评价方法与实证[j]. 统计与决策, 2021, 574(10): 32-36. (cssci)

40. extended belief rule based system with joint learning for environmental governance cost prediction[j]. ecological indicators, 2020, 111: 106070. (ssci & sci)

41. extended belief rule-based model for environmental investment prediction with indicator ensemble selection[j]. international journal of approximate reasoning, 2020, 126: 290-307. (sci & ssci)

42. a new air pollution management method based on the integration of evidential reasoning and slacks - based measure[j]. journal of intelligent & fuzzy systems, 2020, 39(5): 6833-6848. (sci)

43. 基于数据包络分析和扩展置信规则库的交通运输业环境治理成本预测[j]. 交通运输系统工程与信息, 2020, 20(3): 20-27. (ei)

44. 区域环境污染强度测算及其分类治理效率评价研究[j]. 系统科学与数学, 2020, 40(6): 984-1003. (cscd)

45. an environmental pollution management method based on extended belief rule base and data envelopment analysis under interval uncertainty[j]. computers & industrial engineering, 2020, 144: 106454. (sci & ssci)

46. a minimum centre distance rule activation method for extended belief rule-based classification systems [j]. applied soft computing, 2020, 91: 106214. (sci)

47. a structure optimization method for extended belief-rule-based classification system[j]. knowledge-based systems, 2020, 203: 106096. (sci)

48. 考虑投入产出关系与效率的环境治理成本预测方法[j]. 控制与决策, 2020, 35(4): 993-1003. (ei)

49. an interval efficiency evaluation model for air pollution management based on indicators integration and different perspectives[j]. journal of cleaner production, 2020, 245: 118945. (sci & ssci)

50. fuzzy rule based system with feature extraction for environment governance cost prediction[j]. journal of intelligent & fuzzy systems, 2019, 37(2): 2337-2349. (sci & ssci)

51. a new environmental governance cost prediction method based on indicator synthesis and different risk coefficients[j]. journal of cleaner production, 2019, 212: 548-566. (sci & ssci)

52. new product development using disjunctive belief rule base[c]. conference on data science and knowledge engineering for sensing decision support (flins2018), 2018, aug. 21-24, belfast, northern ireland, uk.

53. belief rule base structure and parameter joint optimization under disjunctive assumption for nonlinear complex system modeling[j]. ieee transactions on systems, man, and cybernetics: systems, 2018, 48(9): 1542-1554.(sci)

54. 基于sbm区间模型的决策单元相似度[j]. 控制与决策, 2017, 32(11), 2090-2098. (ei)

55. dynamic rule adjustment approach for optimizing belief rule-base expert system[j]. knowledge -based systems, 2016, 96: 40-60. (sci)

56. belief rule based expert system for classification problems with new rule activation and weight calculation procedures[j]. information sciences, 2016, 336: 75-91. (sci)

57. 基于置信规则库推理的多属性双边匹配决策方法[j]. 南京大学学报(自然科学), 2016, 52(4): 672-681. (cscd)

58. 基于改进置信规则库推理的分类方法[j]. 计算机科学与探索, 2016, 10(5): 709-721. (cscd)

59. 基于差分进化算法的置信规则库推理的分类方法[j]. 中国科学技术大学学报, 2016, 46(9): 764-773. (cscd)

60. 专家干预下置信规则库参数训练的差分进化算法[j]. 计算机科学, 2015, 42(5): 88-93. (cscd)

61. 基于bk树的扩展置信规则库结构优化框架[j]. 计算机科学与探索, 2015, 10(2): 257-267. (cscd)

62. 置信规则库规则约简的粗糙集方法[j]. 控制与决策, 2014, 29(11): 1943-1950. (ei)

63. 面向复杂评价模型的证据推理方法[j]. 模式识别与人工智能, 2014, 27(4): 313-326. (cscd)

64. 数据驱动的置信规则库构建与推理方法[j]. 计算机应用, 2014, 34 (8): 2155-2160, 2169. (cscd)

65. 基于加速梯度求法的置信规则库参数训练方法[j]. 计算机科学与探索, 2014, 8(8): 989-1001. (cscd)

66. 基于变速粒子群优化的置信规则库参数训练方法[j]. 计算机应用, 2014, 34(8): 2161-2165, 2174. (cscd)

（数据更新截止2025年1月）