◎研究方向
1.特征选择 2. 神经网络 3. 机器学习 4. 油田大数据
◎学习与工作经历
1996.9-2000.7，曲阜师范大学数学系，学士
2000.9-2003.7，中国石油大学（华东）计算机学院，硕士 2018.9-2022.12，中国石油大学（华东）控制理论与控制工程，博士研究生 2003.7-2011.5，中国石油大学（华东），数学与计算科学学院，讲师 2011.5-至今，中国石油大学（华东）理学院，讲师
◎主讲课程
主讲本科生必修课《程序设计语言(C++)》《数据结构》《数学实验》《数学基础实践》《大数据基础实训》《Hadoop大数据处理》《神经网络与深度学习》等，研究生必修课《Python语言与数据分析》等。
◎承担和参与项目
近年来，主持或参与的代表性科研项目：
1. 2023-01-01~2026-12-31，基于强化学习的离线-在线交互式油藏开发生产实时优化方法，国家自然科学基金_面上项目，排名2； 2. 2022-01-01~2025-12-01，面向井间连通性的可演化物理导向网络模型研究，国家自然科学基金_面上项目，排名3； 3. 2021-12-01~2022-12-01，基于模型驱动的油藏拟合与优化软件开发服务，横向项目，排名4； 4. 2021-09-08~2021-12-31，基于代理模型的老油田开发指标预测方法技术服务合同，横向项目，排名3； 5. 2021-11-30~2022-01-15，套损井数据库管理系统，横向项目，排名4； 6. 2021-12-06~2021-12-31，非常规地质工程一体化大数据智能建模与优化算法 设计、模块代码加工及测试，横向项目，排名3； 7. 2021-09-08~2021-12-31，基于代理模型的老油田开发指标预测方法，横向项目，排名3； 8. 2021-07-01~2023-06-30，融合渗流机理构建在线机器学习模型的井间连通性研究，省部级其他项目（理工科），排名4； 9. 2020-07-10~2020-11-30，基于油藏数值模拟器代理模型的注采优化方法，横向项目，排名3； 10. 2020-05-01~2022-12-31，融合物理模型及神经网络的可解释油藏连通性研究，自主创新科研计划项目（理工科）_科技专项，排名3； 11. 2020-05-01~2022-12-31，融合物理模型及神经网络的可解释油藏连通性研究，中石油重大科技合作项目，排名4； 12. 2019-12-06~2023-12-31，深层碳酸盐岩油气藏提高储量控制动用方法与技术研究，中石油重大科技合作项目，排名6； 13. 2019-01-01~2022-12-01，基于金银纳米探针的毒害气体富集与比色传感一体化研究与应用，国家自然科学基金_面上项目，排名3； 14. 2015-09-25~2017-06-01，云游网络科技，校级自主创新科研计划项目（理工科），排名1。
◎获奖情况（除教师个人获奖之外，还包含指导学生获奖情况）
指导学生荣获全国大学生数学建模竞赛山东赛区二等奖，省部级，2015-10-01。
◎论文
发表论文情况：
[1] Huaqing Zhang, Yunqi Jiang, Jian Wang, Kai Zhang,Nikhil R. Pal. Bilateral Sensitivity Analysis: A Better Understanding of a Neural Network and Its Application to Reservoir Engineering. International Journal of Machine Learning and Cybernetics, 13, 2135-2152, 2022. (SCI 三区) [2] Huaqing Zhang, Yunqi Jiang, Jian Wang, Kai Zhang, Nikhil R. Pal. Interpretable Neural Networks and Their Application to Inferring Inter-well Connectivity. 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML), pp. 487-491, Hangzhou, China, March 25-27, 2022. (EI) [3] Yunqi Jiang, Huaqing Zhang, Kai Zhang, Jian Wang, Jianfa Han, Shiti Cui, Liming Zhang, Hanjun Zhao, Piyang Liu, Honglin Song. Waterflooding Interwell Connectivity Characterization and Productivity Forecast with Physical Knowledge Fusionn and Model Structure Transfer. Water, 15(2), 218, 2023. (SCI 三区) [4] Yunqi Jiang, Huaqing Zhang, Kai Zhang, Jian Wang, Shiti Cui, Jianfa Han, Liming Zhang, Jun Yao. Reservoir Characterization and Productivity Forecast Based on Knowledge Interaction Neural Network. Mathematics, 10(9), 1614, 2022. (SCI 二区) [5] Xiaopeng Ma, Kai Zhang, Hanjun Zhao, Liming Zhang, Jian Wang, Huaqing Zhang, Piyang Liu, Xia Yan, Yongfei Yang. A vector-to-sequence based multilayer recurrent network surrogate model for history matching of large-scale reservoir. Journal of Petroleum Science and Engineering, 214: 110548, 2022. (SCI 二区 TOP 期刊) [6] Jiamin Li, Xiangyu Wang, Guangdong Xue, Huaqing Zhang, Jian Wang. Sparse Broad Learning System via a Novel Competitive Swarm Optimizer. IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 1697-1701, Beijing, China, October 3-5, 2022. [7] Haochen Wang, Kai Zhang, Xingliang Deng, Shiti Cui, Xiaopeng Ma, Zhongzheng Wang, Ji Qi, Jian Wang, Chuanjin Yao, Liming Zhang, Yongfei Yang, Huaqing Zhang. Highly Accurate Oil Production Forecasting under Adjustable Policy by a Physical Approximation Network. Energy Reports, 8:14396-14415, 2022.(SCI 二区) [8] Chao Zhong, Kai Zhang, Xiaoming Xue, Ji Qi, Liming Zhang, Xia Yan, Huaqing Zhang, Yongfei Yang, Historical Window-Enhanced Transfer Gaussian Process for Production Optimization, SPE Journal, 27 (05): 2895–2912, 12 October 2022. (SCI 三区 TOP 期刊) [9] Huaqing Zhang, Yi-Fei Pu, Xuetao Xie*, Bingran Zhang, Jian Wang*, Tingwen Huang. A global neural network learning machine: Coupled integer and fractional calculus operator with an adaptive learning scheme. Neural Networks, 143: 386-399, 2021. (SCI 二区) [10]Haochen Wang, Jianfa Han, Kai Zhang, Chuanjin Yao, Xiaopeng Ma, Liming Zhang, Yongfei Yang,Huaqing Zhang, Jun Yao, An Interpretable Interflow Simulated Graph Neural Network for Reservoir Connectivity Analysis, SPE J. 26 (04): 1636–1651,DOI: https://doi.org/10.2118/205024-PA, August 2021. (SCI二区) [11]Jian Wang#; Huaqing Zhang#; Junze Wang; Yi-Fei PU*; Nikhil R. Pal; Feature Selection using a Neural Network With Group Lasso Regularization and Controlled Redundancy, IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(3): 1110-1123. (SCI一区，共同一作) [12]Xuetao Xie#, Huaqing Zhang#, Junze Wang, Qin Chang, Jian Wang*, Nikhil R. Pal, Learning optimized structure of neural networks by hidden node pruning with L1 regularization, IEEE Transactions on Cybernetics, 2020, 50(3): 1333-1346. (SCI一区，共同一作) [13]Huaqing Zhang, Jian Wang*, Zhanquan Sun, Jacek M. Zurada, and Nikhil R. Pal; Feature Selection for Neural Networks Using Group Lasso Regularization, IEEE Transactions on Knowledge and Data Engineering, 2020, 32(4): 659-673. (SCI二区) [14]张凯, 赵兴刚, 张黎明, 张华清, 王浩臣, 陈国栋, 赵孟杰, 姜云启, 姚军, 智能油田开发中的大数据及智能优化理论和方法研究现状及展望, 中国石油大学学报(自然科学版), 2020, 44(04): 28-38. (EI) [15]Tao Gao, Jian Wang*, Bingjie Zhang, Huaqing Zhang, Peng Ren, Nikhil R. Pal.A Polak-Ribiere-Polyak Conjugate Gradient-Based Neuro-Fuzzy Network and Its Convergence. IEEE Access, 6: 41551-41565, 2018. (SCI二区) [16]Bingjie Zhang, Junze Wang, Shujun Wu, Jian Wang, Huaqing Zhang*, Fully Complex-Valued Wirtinger Conjugate Neural Networks with Generalized Armijo Search, International Conference on Intelligent Computing (ICIC 2018), 10956: 123-133, Bengaluru, India, 2018-10-25至2018-10-27. (EI) [17]Qin Chang, Junze Wang, Huaqing Zhang, Lina Shi, Jian Wang, Nikhil R. Pal. Structure Optimization of Neural Networks with L1 Regularization on Gates. IEEE Symposium Series on Computational Intelligence, Bangalore, India, pp. 196-203, 2018.(EI) [18]Qin Liu, Zhaoyang Sang, Hua Chen, Jian Wang, Huaqing Zhang*, An Efficient Algorithm for Complex-Valued Neural Networks Through Training Input Weights, International Conference on Neural Information Processing (ICONIP), 10637: 150-159, Guangzhou, China, 2017-11-14至2017-11-18.(EI) [19]Hongmin Gao, Yichen Yang, Bingyin Zhang, Long Li, Huaqing Zhang, Shujun Wu. Feature Selection Using Smooth Gradient L1/2 Regularization. International Conference on Neural Information Processing, 10637: 160-170, 2017. (EI) [20]Huaqing Zhang, Zongmin Li, Yujie Liu, Fractional Orthogonal Fourier-Mellin Moments for Pattern Recognition. Pattern Recognition. CCPR 2016. Communications in Computer and Information Science (2016), vol. 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_62. (EI)
◎著作
参与出版《数据结构与算法》、《数学基础实践》。
◎专利
1.张华清，王健，张凯，姜云启，龚晓玲，薛广东，基于神经网络敏感性分析的井间连通性判断方法及系统，CN202110486296.2，2021年。
2.张凯，姜云启，姚军，刘均荣，张黎明，王健，张华清，姚传进，基于双并联神经网络的机器学习的注采连通性确定方法，CN202011339272.6，2021年。
3.桑兆阳，刘芹，龚晓玲，张华清，陈华，王健，基于梯度下降法与广义逆的复值神经网络训练方法，CN201710091587.5，2017年。 |
