Papers
2
Total Citations
14
H-Index
2
About
Xing Li’s research focuses on advancing computational intelligence for real-time optimization and autonomous robotic systems. Their most impactful contribution is the development of performance-enhancing Zhang Neural Network (ZNN) models for time-variant equality-constraint convex optimization, introducing a transition-state-based attracting system approach that significantly improves convergence and robustness. This 2023 work has already garnered 11 citations, reflecting its growing influence in the field of dynamic optimization. Li also made notable early contributions to mobile robotics, designing a serial communication system based on a client-server structure that enables mobile devices and embedded computers to exchange data efficiently. This system supports autonomous mobile robots in performing multi-sensor information fusion for environmental perception and situational awareness. By bridging theoretical optimization methods with practical robotic communication, Li’s work demonstrates a clear trajectory from foundational engineering solutions to cutting-edge neural network-based control. Their research is particularly relevant for students and researchers interested in real-time optimization, neural dynamics, and intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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