Keming Tang
Papers
1
Total Citations
4
H-Index
1
About
Keming Tang is a researcher focused on the intersection of robotics, neural networks, and control systems, particularly in 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 critical challenge in robotics: the difficulty of modeling robot dynamics due to parametric uncertainties and modeling errors. Tang proposes an innovative eigen-based neural network approach to predict and analyze controller parameters, enabling more adaptive and robust performance in tasks like grinding. This work, with 4 citations, lays foundational groundwork for integrating neural network theory with real-world robotic control. Tang’s contributions are notable for tackling the "black box" problem of neural networks in dynamic systems, offering a structured method to relate network structural changes to environmental inputs and outputs. His research holds promise for advancing autonomous robotics in manufacturing, where precision and adaptability are paramount. For students and researchers, Tang’s work exemplifies how theoretical neural network solutions can directly enhance practical robotic applications.
Research Focus
Key Achievements
Top Papers
- 1