姓名: 黎倩 职称: 副教授

邮箱: qianli@ncu.edu.cn 导师类型: 硕导

主讲课程: 误差理论与数据处理、矩阵分析等

个人主页:

研究方向

主要开展人工智能、深度学习、神经网络设计、基于数据驱动的时间序列建模等研究。

教育经历

2017.09 – 2020.12, 重庆大学，控制理论与控制工程，博士

2014.09 – 2017.07, 重庆大学，控制科学与工程，硕士

工作履历

2024.01 ~ 至今, 南昌大学，先进制造学院，副教授

2022.01 ~ 2023.12, 南昌大学，先进制造学院，讲师

2021.01 ~ 2021.12, 南昌大学，信息工程学院，讲师

主要业绩

黎倩，博士，副教授，硕导。主持国家自然科学基金项目1项，江西省自然科学基金2项和南昌大学香樟育才项目1项。在国际主流权威期刊和会议上发表高水平学术论文20余篇，其中，中科院一区/二区论文10余篇，包括IEEE TNNLS、Applied Energy、IEEE TII等领域Top期刊；授权和申请国家发明专利多项。

· 科研项目：

1) 国家自然科学基金委员会, 地区科学基金项目, 62163026, 基于多尺度信息融合的多任务回声状态网络太阳辐照度预测研究, 2022-01-01 至 2025-12-31, 36万元, 主持

2) 江西省自然科学基金, 面上项目, 20242BAB25090, 基于自适应深度编码回声状态网络的时间序列多步预测研究, 2024-06-01 至 2026-5-31, 10万元, 主持

3) 江西省自然科学基金, 青年基金, 20224BAB212018, 多优化策略协调下的卷积神经网络太阳辐照度预测研究, 2023-01-01 至 2025-12-31, 10万元, 主持

4) 南昌大学人才项目，香樟育才，2021-01-01 至 2025-12-31, 10万元,主持

· 科研论文：

1. Li T, Guo Z J, Li Q*. Decomposition based deep projection-encoding echo state network for multi-scale and multi-step wind speed prediction [J]. Expert Systems With Applications, 2025, 266: 126074. (通讯作者 SCI 中科院1区TOP)

2. Li T, Guo Z J, Li Q*. Deep echo state network with projection-encoding for multi-step time series prediction [J]. Neurocomputing, 2025, 617: 128939. (通讯作者 SCI 中科院2区TOP)

3. Wu Z, Li Q*, Zhang H J. Chain-structure echo state network with stochastic optimization: methodology and application [J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(5): 1974-1985. (通讯作者 SCI 中科院1区TOP)

4. Li Q, Wu Z, Ling R, et al. Multi-reservoir echo state computing for solar irradiance prediction: a fast yet efficient deep learning approach [J]. Applied Soft Computing, 2020, 95: 106481. (第一作者 SCI 中科院1区TOP)

5. Li Q, Wu Z, Zhang H J. Spatio-temporal modeling with enhanced flexibility and robustness of solar irradiance prediction: A chain-structure echo state network approach [J]. Journal of Cleaner Production, 2020, 261: 121151. (第一作者 SCI 中科院1区TOP)

6. Li Q, Wu Z, Xia X H. Estimate and characterize PV power at demand-side hybrid system [J]. Applied Energy, 2018, 218: 66-77. (第一作者 SCI 中科院1区TOP)

7. Guo Z J, Zeng L W, Xiong P W, Li Q*, “Distributed GNE seeking for aggregative games under event-triggered communication: Predefined-time convergent algorithm design”, Neurocomputing, 2025, 657:131623. (通讯作者 SCI 中科院2区TOP)

8. Wu Z, Li Q, Xia X H. Multi-timescale forecast of solar irradiance based on multi-task learning and echo state network approaches [J]. IEEE Transactions on Industrial Informatics, 2021, 17(1): 300-310. (SCI 中科院1区TOP)

9. Wu Z, Li Q, Wu W, et al. Crowdsourcing model for energy efficiency retrofit and mixed-integer equilibrium analysis [J]. IEEE Transactions on Industrial Informatics, 2020, 16(7): 4512-4524. (SCI 中科院1区TOP)

10. Li Q, Hong B Y, Guo Z J*, “Distributed predefined-time zero-gradient-sum optimization for networked systems: From continuous-time to event-triggered communications”, International Journal of Control, Automation and Systems, 2025, 23(5):1389-1401. (第一作者 SCI)

11. 郭志军, 曾令伟, 洪宝源, 黎倩*，李志勇. “动态事件触发通信下分布式预定时间非光滑约束优化算法”, 控制与决策, 2025, 40(6): 2032-2040. (通讯作者EI期刊)

12. Xiong P, Zhou X, Li Q*, et al. Path prediction of flexible needles based on Fokker-Planck equation and disjunctive Kriging model [J]. Journal of Southeast University (English Edition), 2022, 38(2): 118-125. (通讯作者EI期刊)

13. Guo Z J, Xin J L, Li Q*, “Exponentially convergent algorithms design for distributed resource allocation under non-strongly convex condition: from continuous-time to event-triggered communication”, IEEE transactions on industrial cyber-physical systems, 2025, 3:127-138. (通讯作者EI期刊)

14. Li. T, Guo Z J, Li. Q*, Multi-scale deep echo state network for time series prediction [J], Neural Computing and Applications, 2024, 36(21):13305-13325.

15. Yang D Y, Li T, Guo Z J, Li Q*, “Multi-scale convolutional echo state network with an effective pre-training strategy for solar irradiance forecasting”, IEEE Access, 2024,12:13442-13452.

16. Xue F, Li Q, Li X. Reservoir Computing with Both Neuronal Intrinsic Plasticity and Multi-Clustered Structure [J]. Cognitive Computation, 2017, 9(3): 400-410.