Xianzhi Deng
Papers
6
Total Citations
131
H-Index
6
About
Xianzhi Deng is a leading researcher in robotics and neural network control, specializing in motion planning for redundant robot manipulators. His core contributions lie in developing bio-inspired neural networks that resist periodic and cognitive noises—a critical challenge in real-world robotic systems. Deng pioneered the circadian rhythms neural network (CRNN) and its learning variant (CRLN), which mimic biological circadian cycles to cancel out time-varying periodic disturbances. His work has been validated through FPGA implementations, demonstrating practical, pipeline-based hardware solutions for noise-robust robot control. With over 130 citations across his most influential papers—including a 32-citation study on CRLN for time-varying dynamic systems—Deng’s impact is evident in both theory and application. He has also advanced varying-parameter recurrent neural networks with super-exponential convergence rates, as summarized in his 2022 review. Notably, his Runge–Kutta type discrete CRNN model for tri-criteria optimization showcases his ability to integrate numerical methods with neural dynamics. Deng’s research bridges the gap between biological inspiration and engineering robustness, offering students and engineers a powerful framework for designing resilient autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 6A review on varying-parameter convergence differential neural network14 citations · 2022