Zhengyang Shang
Papers
1
Total Citations
4
H-Index
1
About
Zhengyang Shang’s research lies at the intersection of robotics, neural networks, and intelligent control systems, with a particular focus on adaptive modeling for complex, uncertain environments. His most-cited work, “Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments” (2019), addresses a fundamental challenge in robotics: the difficulty of obtaining accurate dynamic models due to parametric uncertainties and modeling errors. Shang proposes a novel eigen solution approach that reveals how changes in neural network structure correspond to shifts in input-output behavior, enabling more robust prediction and tuning of controller parameters for grinding robots operating under unpredictable conditions. While his citation count is still growing, this work demonstrates his early contribution to bridging theoretical neural network analysis with practical robotic control. Shang’s research is particularly relevant for students and engineers working on adaptive control, intelligent manufacturing, and the application of neural networks to real-world robotic systems where environmental variability poses significant challenges.
Research Focus
Key Achievements
Top Papers
- 1