Zhijun Tang
Papers
3
Total Citations
88
H-Index
3
About
Zhijun Tang is a leading researcher in computational intelligence and robotics, specializing in zeroing neural networks (ZNN) for solving time-varying problems. His major contributions lie in developing novel activation functions and convergence frameworks that dramatically improve the speed and robustness of ZNN models, with direct applications to dynamic quadratic programming, matrix equation solving, and robotic control. His most-cited work (2022, 63 citations) introduces a predefined fixed-time convergence ZNN using a power piecewise activation function, enabling dual-arm manipulators to achieve cooperative trajectory tracking with unprecedented precision and reliability. Tang further advanced the field by proposing nonlinear ZNNs for time-varying linear matrix equations and electronic circuit current computing (2022, 19 citations), and a fast-convergence ZNN for dynamic Sylvester equations with applications in robot trajectory tracking (2023, 6 citations). His research bridges theoretical neural dynamics and practical engineering, offering efficient solutions for real-time optimization and multi-robot coordination. With a growing citation impact, Tang’s work is essential reading for researchers in neural computation, control systems, and robotics, demonstrating how mathematical innovation can drive tangible advances in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3