Mingxing Duan
Papers
2
Total Citations
22
H-Index
1
About
Mingxing Duan is a leading researcher in computational intelligence and robotics, with a primary focus on neural dynamics, numerical optimization, and autonomous mobile manipulation. His most influential work introduces Adams–Bashforth-type discrete-time zeroing neural networks (ADTIZD) to solve time-varying complex Sylvester equations—a critical problem in real-time control and signal processing. By integrating Adams–Bashforth discrete formulas into zeroing neural dynamics, Duan achieved enhanced robustness and accuracy, pioneering a new class of discrete-time solvers. This work has garnered 21 citations, underscoring its impact on advancing neural network-based computation for time-varying systems. Duan also contributes to robotics through the development of CSubBT, a modular execution framework with self-adjusting capability for mobile manipulation systems. This framework enables adaptive task planning and execution, addressing key challenges in dynamic environments. His research bridges theoretical neural dynamics with practical robotic applications, offering robust solutions for complex, time-sensitive problems. Duan’s work is essential reading for researchers in neural computing, control theory, and robotics, demonstrating how discrete-time models can enhance both theoretical understanding and real-world system performance.
Research Focus
Key Achievements
Top Papers
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- 2