Papers
6
Total Citations
308
H-Index
6
About
Zhuo-Yun Nie is a leading researcher in neural dynamics and robotic control, whose work has fundamentally advanced the real-time solution of time-varying mathematical systems. His primary research areas include zeroing neural networks (ZNN), noise-tolerant Zhang dynamics (ZD), and recurrent neural networks (RNN) for kinematic control and obstacle avoidance in redundant robot manipulators. Nie’s most impactful contribution is the development of a ZNN model for solving time-varying linear equations and inequality systems (101 citations), providing a robust framework for real-time problem-solving in dynamic environments. He further extended this work by introducing a noise-tolerant ZD design formula, applying it to robots’ kinematic control via time-varying nonlinear equations (84 citations), and proposing the first RNN model for time-dependent underdetermined linear systems with bound constraints (43 citations). His discrete-time RNN for dynamic nonlinear equations (46 citations) and noise-tolerant obstacle avoidance schemes (20 citations) have set new standards in robotic motion planning. Notable for his innovative use of Li-function activated RNNs for acceleration-level control (14 citations), Nie’s research bridges theoretical neural dynamics with practical robotics, earning him recognition as a pioneer in noise-resilient, real-time computational methods.
Research Focus
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Top Papers
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