Papers
2
Total Citations
23
H-Index
2
About
Mingzhen He is a researcher specializing in robotics, neural network optimization, and motion planning for redundant robot manipulators. His work focuses on developing advanced discrete-time neural network models that can resist periodic noise interference—a critical challenge in real-world robotic hardware and environments. He has introduced innovative approaches such as the Runge–Kutta type discrete circadian rhythms neural network (RK-DCRNN) and the Taylor discrete circadian rhythms neural network (TD-CRNN), which solve bicriteria and tri-criteria optimization problems for noise-perturbed manipulators. These contributions are vital for achieving precise, stable, and optimal control in automated systems. His most-cited paper, “Runge–Kutta Type Discrete Circadian RNN for Resolving Tri-Criteria Optimization Scheme of Noises Perturbed Redundant Robot Manipulators” (2020), has garnered 20 citations, reflecting its impact on the field. He continues to push the boundaries of neural network-based motion planning, offering robust solutions for industrial and service robots operating under challenging conditions.
Research Focus
Key Achievements
Top Papers
- 1
- 2