Yihong Ling
Papers
2
Total Citations
50
H-Index
2
About
Yihong Ling’s research lies at the intersection of neural dynamics, robotics, and numerical computation, with a particular focus on advancing Zhang neural network (ZNN) theory for real-time control and matrix computation. Ling’s most influential work, “Inverse-Free Discrete ZNN Models Solving for Future Matrix Pseudoinverse via Combination of Extrapolation and ZeaD Formulas” (2020, 43 citations), introduced a novel framework for handling time-varying matrix pseudoinverse problems. By leveraging Zhang matrix concepts and discrete-time formulas, this work overcame limitations of conventional models like the Getz-Marsden dynamic model, offering a more robust and inverse-free solution—a critical contribution for real-time applications in engineering and signal processing. Ling also made notable strides in robotics with “Robust Zhang Neural Network for Tracking Control of Parallel Robot Manipulators With Unknown Parameters” (2019, 7 citations), which addressed the challenging problem of parameter uncertainty in robot control. Unlike traditional ZNN approaches that assume perfect knowledge of robot parameters, Ling’s robust ZNN model enables accurate tracking control even when system parameters are unknown or varying. This work has practical implications for industrial automation and advanced robotic systems. With a growing citation record and a focus on bridging theoretical neural dynamics with real-world engineering challenges, Ling’s research continues to influence both the mathematical foundations and applied domains of intelligent control systems.
Research Focus
Key Achievements
Top Papers
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