Name: Qian Li Title: Associate Professor

Email: qianli@ncu.edu.cn

Supervisor Type: Master's Supervisor

Main Courses: Error theory and data processing、Matrix Analysis

Research Directions

The main research focuses on artificial intelligence, deep learning, neural network design, and data-driven time series modeling.

Education Background

Sep 2017 – Dec 2020, Chongqing University，Control theory and control engineering，Doctor

Sep 2014 – Jul 2017, Chongqing University，Control Science and engineering，Master

Work Experience

Jan 2024 ~ Present, Nanchang University，School of Advanced Manufacturing，Associate Professor

Jan 2022 ~ Dec 2023, Nanchang University，School of Advanced Manufacturing，Lecturer

Jan 2021 ~ Dec 2021, Nanchang University，School of Nanchang University，School of Advanced Manufacturing，Lecturer

Key Achievements

Qian Li, Ph.D., Associate Professor, Master's Supervisor. She has served as principal investigator for one National Natural Science Foundation (NSFC) project, two Jiangxi Provincial Natural Science Foundation projects, and one Nanchang University Talent Project. She has published over 20 high-level academic papers in international authoritative journals and conferences, including more than 10 Q1/Q2 papers according to the Chinese Academy of Sciences (CAS), in top-tier journals such as IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Applied Energy, and IEEE Transactions on Industrial Informatics (TII). She holds multiple authorized national invention patents and has applied for additional ones.

· Research Projects：

1) Multi-task Echo State Network with Multi-scale Information Fusion for Solar Irradiance Forecasting, Regional Science Fund of the National Natural Science Foundation of China, 62163026, 2022-01-01-2025-12-31, PI

2) Adaptive Deep Encoding in Echo State Networks for Multi-step Time Series Forecasting, General Program of Jiangxi Provincial Natural Science Foundation, 20242BAB25090,2024-06-01-2026-5-31, PI

3) Coordinated Multi-Strategy Optimization Enhanced CNN for Solar Irradiance Forecasting, Youth Foundation of Jiangxi Provincial Natural Science Foundation, 20224BAB212018, 2021-01-01-2025-12-31, PI

4) Nanchang University Talent Project, 2021-01-01-2025-12-31, PI

· Research Published Papers：

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. (Corresponding Author Chinese Academy of Sciences SCI 1 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. (Corresponding Author Chinese Academy of Sciences SCI 2 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. (Corresponding Author Chinese Academy of Sciences SCI 1 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. (First Author Chinese Academy of Sciences SCI 1 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. (First Author Chinese Academy of Sciences SCI 1 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. (First Author Chinese Academy of Sciences SCI 1 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. (Corresponding Author Chinese Academy of Sciences SCI 2 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. (Chinese Academy of Sciences SCI 1 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. (Chinese Academy of Sciences SCI 1 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. (First Author SCI)

11. Guo Z J, Zeng L W, Hong B Y, Li Q*，Li Z Y. Distributed non-smooth constrained optimization: A predefined-time and dynamic event-triggered approach, Control and Decision, 2025, 40(6): 2032-2040. (Corresponding Author EI Journal)

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. (Corresponding Author EI Journal)

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. (Corresponding Author EI Journal)

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.