基本信息
杨隆浩
讲师



研究方向
证据推理
置信规则库推理
环境治理成本预测
可解释性的机器学习
数据驱动建模与智能决策

讲授课程


联系方式
电子邮箱：more026@hotmail.com



个人简介
杨隆浩，博士，硕士生导师，讲师，主要从事证据推理、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等多个科研项目，并发表四十多篇国内外学术论文。获邀担任《系统工程理论与实践》《IEEE Transactions on Fuzzy Systems》《Knowledge-Based Systems》《Information Sciences》《Information Fusion》《International Journal of Approximate Reasoning》等多个国内外期刊的同行评议专家。
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（专业型）。
招生要求：有拿硕士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成果奖励，推荐海外攻读博士学位（含奖学金）。

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
2019 - 至 今，福州大学经济与管理学院，讲师
2019 - 2020，英国阿尔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

科研项目
福建省社科规划项目，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
教育部人文社科项目，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
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
国家自然科学基金项目，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

获奖经历
2019年，福建省第十三届社会科学优秀成果奖三等奖（排名第一）
2019年，福州大学优秀博士学位论文
2019年，福州大学研究生高水平学术成果奖
2019年，福州大学优秀博士毕业生
2018年，博士研究生国家奖学金
2017年，博士研究生国家奖学金
2015年，福州大学优秀硕士学位论文
2015年，福州大学优秀硕士毕业生
2014年，硕士研究生国家奖学金

近年发表的主要论文
◆ 一作论文
[1]Long-Hao Yang, F.-F. Ye, J. Liu, Y.-M. Wang, H. Hu, An Improved Fuzzy Rule-based System using Evidential Reasoning and Subtractive Clustering for Environmental Investment Prediction[J]. Fuzzy Sets and Systems, 2021, In Press. (SCI, IF: 3.305, 中科院分区一区)
[2]Long-Hao Yang, S. Wang, F.-F. Ye, J. Liu, Y.-M. Wang, H. Hu, Environmental Investment Prediction using Extended Belief Rule-Based System and Evidential Reasoning Rule[J]. Journal of Cleaner Production, 2021, 289: 1-19. (SCI & SSCI, IF: 7.246, 中科院分区一区)
[3]Long-Hao Yang, J. Liu, Y.-M. Wang, L. Martínez, 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: 9.309, 中科院分区一区)
[4]杨隆浩, 叶菲菲, 王应明. 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)
[5]Long-Hao Yang, F.-F. Ye, Y.-M. Wang, Ensemble Belief Rule Base Modeling with Diverse Attribute Selection and Cautious Conjunctive Rule for Classification Problems[J]. Expert Systems with Applications, 2020, 146: 1-14. (SCI, IF: 5.452, 中科院分区一区)
[6]Long-Hao Yang, J. Liu, Y.-M. Wang, L. Martínez, New activation weight calculation and parameter optimization for extended belief rule-based system based on sensitivity analysis[J]. Knowledge and Information System, 2019, 60: 837-878. (SCI, IF: 2.936, 中科院分区三区)
[7]Long-Hao Yang, J. Liu, Y.-M. Wang, L. Martínez, 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: 4.873, 中科院分区二区)
[8]Long-Hao Yang, J. Liu, Y.-M. Wang, L. Martínez, 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. (EI)
[9]Long-Hao Yang, Y.-M. Wang, Y.-G. Fu, A consistency analysis-based rule activation method for extended belief rule base system[J]. Information Sciences, 2018, 445-446: 50-65. (SCI, IF: 5.524, 中科院分区一区)
[10]Long-Hao Yang, Y.-M. Wang, J. Liu, L. Martínez, A Joint Optimization Method on Parameter and Structure for Belief-Rule-Based Systems[J]. Knowledge-Based Systems, 2018, 142: 220-240. (SCI, IF: 5.101, 中科院分区二区)
[11]Long-Hao Yang, Y.-M. Wang, L.-L. Chang, Y.-G. Fu, 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, 中科院分区二区)
[12]Long-Hao Yang, Y.-M. Wang, Y.-X. Lan, L. Chen, Y.-G. Fu, 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: 4.396, 中科院分区二区)
[13]Long-Hao Yang, Y.-M. Wang, Q. Su, Y.-G. Fu, K.-S. Chin, Multi-attribute search framework for optimizing extended belief rule-based systems[J]. Information Sciences, 2016, 370-371: 159-183. (SCI, IF: 4.832, 中科院分区一区)
[14]杨隆浩, 王晓东, 傅仰耿, 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)
[15]杨隆浩, 傅仰耿, 巩晓婷, 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.
[16]杨隆浩, 蔡芷铃, 黄志鑫, 何星, 傅仰耿, 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)
[17]杨隆浩, 傅仰耿, 吴英杰, 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
[1]叶菲菲, 杨隆浩, 王应明, 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, 已录用. (CSCD)
[2]F.-F. Ye, S. Wang, Long-Hao Yang, Y. M. Wang, 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, IF: 1.851, 中科院分区四区)
[3]Y.-M. Wang, F.-F. Ye, Long-Hao Yang, Extended belief rule based system with joint learning for environmental governance cost prediction[J]. Ecological Indicators, 2020, 111: 1-14. (SCI&SSCI, IF: 4.229, 中科院分区二区)
[4]F.-F. Ye, Long-Hao Yang, Y. M. Wang, L. Chen, An environmental pollution management method based on extended belief rule base and data envelopment analysis under interval uncertainty[J], Computers & Industrial Engineering, 2020, 144: 1-15. (SCI&SSCI, IF: 4.135, 中科院分区二区)
[5]F.-F. Ye, S. Wang, P. Nicholl, Long-Hao Yang, Y.-M. Wang, 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, IF: 2.678, 中科院分区三区)
[6]叶菲菲, 杨隆浩, 王应明, 蓝以信, 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)
[7]叶菲菲, 杨隆浩, 王应明. 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)
[8]H.-Z. Zhu, M.-Q. Xiao, Long-Hao Yang, X.-L. Tang, Y.-J. Liang, J.-F. Li, A Minimum Centre Distance Rule Activation Method for Extended Belief Rule-Based Classification Systems[J]. Applied Soft Computing, 2020, 91: 1-14. (SCI, IF: 4.873, 中科院分区二区)
[9]H.-Z. Zhu, M.-Q. Xiao, X. Zhao, X.-L. Tang, Long-Hao Yang, W.-J. Kang, Z.-Z. Liu, A structure optimization method for extended belief-rule-based classification system, Knowledge-Based Systems, 2020, 203: 1-15. (SCI, IF: 5.921, 中科院分区二区)
[10]叶菲菲, 杨隆浩, 王应明. 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)
[11]F.-F. Ye, Long-Hao Yang, Y.-M. Wang, An Interval Efficiency Evaluation Model for Air Pollution Management Based on Indicators Integration and Different Perspectives[J]. Journal of Cleaner Production, 2020, 245: 1-15. (SCI&SSCI, IF: 7.246, 中科院分区一区)
[12]F.-F. Ye, Long-Hao Yang, Y.-M. Wang, 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, IF: 1.851, 中科院分区四区)
[13]F.-F. Ye, Long-Hao Yang, Y.-M. Wang, 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, IF: 7.246, 中科院分区一区)
[14]J.-G. Xu, M.-J. Li, L.-L. Chang, J. Jiang, Y.-W. Chen, Long-Hao Yang, New Product Development using Disjunctive Belief Rule Base[C] Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018), 2018, August 21-24, Belfast, Northern Ireland, UK. (EI)
[15]L.-L. Chang, Z.-J. Zhou, Y.-W. Chen, T.-J. Liao, Y. Hu, Long-Hao Yang, 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, IF: 7.351, 中科院分区一区)
[16]黄衍, 王应明, 杨隆浩, 基于SBM区间模型的决策单元相似度, 控制与决策, 2017, 32(11), 2090-2098. (EI)
[17]Y.-M. Wang, Long-Hao Yang, Y.-G. Fu, L.-L. Chang, K.-S. Chin, Dynamic rule adjustment approach for optimizing belief rule-base expert system[J]. Knowledge-Based Systems, 2016, 96: 40-60. (SCI, IF: 4.529, 中科院分区二区)
[18]L.-L. Chang, Z.-J. Zhou, Y. You, Long-Hao Yang, Z.-G. Zhou, Belief rule based expert system for classification problems with new rule activation and weight calculation procedures[J]. Information Sciences, 2016, 336: 75-91. (SCI, IF: 4.832, 中科院分区一区)
[19]方志坚, 杨隆浩, 傅仰耿, 陈建华, 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)
[20]叶青青, 杨隆浩, 傅仰耿, 陈晓聪, 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)
[21]刘莞玲, 王韩杰, 傅仰耿, 杨隆浩, 吴英杰, 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)
[22]王韩杰, 杨隆浩, 傅仰耿, 吴英杰, 巩晓婷, 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)
[23]苏群, 杨隆浩, 傅仰耿, 余瑞银, 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)
[24]王应明, 杨隆浩, 常雷雷, 傅仰耿, 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)
[25]傅仰耿, 杨隆浩, 吴英杰, 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)
[26]余瑞银, 杨隆浩, 傅仰耿, 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)
[27]吴伟昆, 杨隆浩, 傅仰耿, 张立群, 巩晓婷, 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)
[28]苏群, 杨隆浩, 傅仰耿, 吴英杰, 巩晓婷, 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)
[29]杨小玲, 甘文勇, 杨隆浩, 王一蕾, 傅仰耿, 基于Web Services的旅游信息集成技术[J]. 福州大学学报(自然科学版), 2013, 4 (2), 178-181+201. (CSCD)
（数据更新截止2021年2月）

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