杨隆浩
基本信息
杨隆浩，副教授，硕导
研究方向
理论方面：
1. 证据推理
2. 数据驱动建模
3. 置信规则库推理
4. 可解释性的机器学习
应用方面：
1. 环境治理成本预测
2. 智能家居中活动识别
3. 双碳目标下碳达峰预测
4. 非均衡数据下临床诊断
1. 证据推理
2. 数据驱动建模
3. 置信规则库推理
4. 可解释性的机器学习
应用方面：
1. 环境治理成本预测
2. 智能家居中活动识别
3. 双碳目标下碳达峰预测
4. 非均衡数据下临床诊断
讲授课程
本科生：《电子服务》
研究生：《智能算法与应用》
研究生：《智能算法与应用》
j9九游会真人的联系方式
- 通信地址： 福州大学旗山校区经管西楼
- 电子邮箱： more026@hotmail.com
个人简介
杨隆浩，副教授，博士，硕士生导师，旗山学者，福建省高层次现有人才，香港理工大学杰出博士后，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，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60余篇国内外学术论文，其中sci/ssci收录期刊论文30余篇。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）、工业工程与管理（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）。
团队成果：idea team团队j9九游会登陆入口主页（），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
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
2022 - 至今，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
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
科研项目
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，在研
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，结题
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，在研
福州大学研究生教改项目，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
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，结题
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，结题
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，在研
福州大学研究生教改项目，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
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，结题
获奖经历
2021年，福建省高层次现有人才
2020年，福州大学优秀博士学位论文
2019年，福建省第十三届社科优秀成果奖三等奖（排名第一）
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
2020年，福州大学优秀博士学位论文
2019年，福建省第十三届社科优秀成果奖三等奖（排名第一）
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, et al., 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, 中科院分区1区, 计算机科学top期刊)
[2] long-hao yang, et al., 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, 中科院分区1区, 计算机科学top期刊)
[3] 杨隆浩, 等. 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)
[4] long-hao yang, et al., an ensemble extended belief rule base decision model for imbalanced classification problems[j]. knowledge-based systems, 2022, 242: 108410. (sci, if: 8.038, 中科院分区1区, jcr分区1区, 计算机科学top期刊)
[5] long-hao yang, et al., 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, jcr 1区, 计算机科学top期刊)
[6] long-hao yang, et al., 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, 中科院分区3区, jcr分区2区)
[7] long-hao yang, et al., 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, jcr 2区)
[8] long-hao yang, et al., 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)
[9] long-hao yang, et al., online updating extended belief rule-based system for sensor - based activity recognition[j]. expert systems with applications, 2021, 186: 115737. (sci, if: 6.954, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[10] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 数学学科top期刊)
[11] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 环境科学与生态学学科top期刊)
[12] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[13] 杨隆浩, 等. 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)
[14] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[15] long-hao yang, et al., 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, 中科院分区3区, jcr分区2区)
[16] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[17] long-hao yang, et al., 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.
[18] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[19] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[20] long-hao yang, et al., 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2区)
[21] long-hao yang, et al., 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, jcr 1区, 计算机科学top期刊)
[22] long-hao yang, et al., multi-attribute search framework for optimizing extended belief rule-based systems[j]. information sciences, 2016, 370-371: 159-183. (sci, if: 5.910, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[23] 杨隆浩, 等. 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)
[24] 杨隆浩, 等. 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.
[25] 杨隆浩, 等. 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)
[26] 杨隆浩, 等. 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
[27] long-hao yang (三作). 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)
[28] long-hao yang (二作). 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)
[29] long-hao yang (五作). 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)
[30] long-hao yang (三作). 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)
[31] 杨隆浩 (二作). 面向国际化的“采购与供应管理”课程教学改革研究[j]. 黑龙江教育(高教研究与评估), 2022, 4: 37- 42.
[32] 杨隆浩 (二作). 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)
[33] 杨隆浩 (二作). 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)
[34] long-hao yang (三作). extended belief rule based system with joint learning for environmental governance cost prediction[j]. ecological indicators, 2020, 111: 106070. (ssci & sci)
[35] long-hao yang (四作). 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)
[36] long-hao yang (三作). 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)
[37] 杨隆浩 (二作). 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)
[38] 杨隆浩 (二作). 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)
[39] long-hao yang (二作). 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)
[40] long-hao yang (三作). a minimum centre distance rule activation method for extended belief rule-based classification systems [j]. applied soft computing, 2020, 91: 106214. (sci)
[41] long-hao yang (五作). a structure optimization method for extended belief-rule-based classification system[j]. knowledge-based systems, 2020, 203: 106096. (sci)
[42] 杨隆浩 (二作). 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)
[43] long-hao yang (二作). 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)
[44] long-hao yang (二作). 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)
[45] long-hao yang (二作). 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)
[46] long-hao yang (六作). 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.
[47] long-hao yang (六作). 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)
[48] 杨隆浩 (三作). 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)
[49] long-hao yang (二作). dynamic rule adjustment approach for optimizing belief rule-base expert system[j]. knowledge -based systems, 2016, 96: 40-60. (sci)
[50] long-hao yang (四作). belief rule based expert system for classification problems with new rule activation and weight calculation procedures[j]. information sciences, 2016, 336: 75-91. (sci)
[51] 杨隆浩 (二作). 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)
[52] 杨隆浩 (二作). 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)
[53] 杨隆浩 (四作). 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)
[54] 杨隆浩 (二作). 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)
[55] 杨隆浩 (二作). 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)
[56] 杨隆浩 (二作). 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)
[57] 杨隆浩 (二作). 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)
[58] 杨隆浩 (二作). 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)
[59] 杨隆浩 (二作). 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)
[60] 杨隆浩 (二作). 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)
[61] 杨隆浩 (三作). 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2023年4月）
[1] long-hao yang, et al., 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, 中科院分区1区, 计算机科学top期刊)
[2] long-hao yang, et al., 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, 中科院分区1区, 计算机科学top期刊)
[3] 杨隆浩, 等. 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)
[4] long-hao yang, et al., an ensemble extended belief rule base decision model for imbalanced classification problems[j]. knowledge-based systems, 2022, 242: 108410. (sci, if: 8.038, 中科院分区1区, jcr分区1区, 计算机科学top期刊)
[5] long-hao yang, et al., 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, jcr 1区, 计算机科学top期刊)
[6] long-hao yang, et al., 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, 中科院分区3区, jcr分区2区)
[7] long-hao yang, et al., 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, jcr 2区)
[8] long-hao yang, et al., 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)
[9] long-hao yang, et al., online updating extended belief rule-based system for sensor - based activity recognition[j]. expert systems with applications, 2021, 186: 115737. (sci, if: 6.954, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[10] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 数学学科top期刊)
[11] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 环境科学与生态学学科top期刊)
[12] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[13] 杨隆浩, 等. 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)
[14] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[15] long-hao yang, et al., 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, 中科院分区3区, jcr分区2区)
[16] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[17] long-hao yang, et al., 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.
[18] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[19] long-hao yang, et al., 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, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[20] long-hao yang, et al., 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2区)
[21] long-hao yang, et al., 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, jcr 1区, 计算机科学top期刊)
[22] long-hao yang, et al., multi-attribute search framework for optimizing extended belief rule-based systems[j]. information sciences, 2016, 370-371: 159-183. (sci, if: 5.910, 中科院分区1区, jcr分区1区, 计算机科学学科top期刊)
[23] 杨隆浩, 等. 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)
[24] 杨隆浩, 等. 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.
[25] 杨隆浩, 等. 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)
[26] 杨隆浩, 等. 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
[27] long-hao yang (三作). 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)
[28] long-hao yang (二作). 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)
[29] long-hao yang (五作). 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)
[30] long-hao yang (三作). 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)
[31] 杨隆浩 (二作). 面向国际化的“采购与供应管理”课程教学改革研究[j]. 黑龙江教育(高教研究与评估), 2022, 4: 37- 42.
[32] 杨隆浩 (二作). 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)
[33] 杨隆浩 (二作). 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)
[34] long-hao yang (三作). extended belief rule based system with joint learning for environmental governance cost prediction[j]. ecological indicators, 2020, 111: 106070. (ssci & sci)
[35] long-hao yang (四作). 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)
[36] long-hao yang (三作). 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)
[37] 杨隆浩 (二作). 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)
[38] 杨隆浩 (二作). 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)
[39] long-hao yang (二作). 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)
[40] long-hao yang (三作). a minimum centre distance rule activation method for extended belief rule-based classification systems [j]. applied soft computing, 2020, 91: 106214. (sci)
[41] long-hao yang (五作). a structure optimization method for extended belief-rule-based classification system[j]. knowledge-based systems, 2020, 203: 106096. (sci)
[42] 杨隆浩 (二作). 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)
[43] long-hao yang (二作). 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)
[44] long-hao yang (二作). 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)
[45] long-hao yang (二作). 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)
[46] long-hao yang (六作). 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.
[47] long-hao yang (六作). 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)
[48] 杨隆浩 (三作). 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)
[49] long-hao yang (二作). dynamic rule adjustment approach for optimizing belief rule-base expert system[j]. knowledge -based systems, 2016, 96: 40-60. (sci)
[50] long-hao yang (四作). belief rule based expert system for classification problems with new rule activation and weight calculation procedures[j]. information sciences, 2016, 336: 75-91. (sci)
[51] 杨隆浩 (二作). 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)
[52] 杨隆浩 (二作). 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)
[53] 杨隆浩 (四作). 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)
[54] 杨隆浩 (二作). 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)
[55] 杨隆浩 (二作). 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)
[56] 杨隆浩 (二作). 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)
[57] 杨隆浩 (二作). 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)
[58] 杨隆浩 (二作). 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)
[59] 杨隆浩 (二作). 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)
[60] 杨隆浩 (二作). 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)
[61] 杨隆浩 (三作). 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2023年4月）
出版著作
杨隆浩（一作），置信规则库的建模新方法与应用[m]. 科学出版社
杨隆浩（二作），中国环境治理效率评价及成本预测[m]. 经济科学出版社
杨隆浩（二作），中国环境治理效率评价及成本预测[m]. 经济科学出版社