Papers
7
Total Citations
138
H-Index
7
About
Haoen Huang is a leading researcher in computational intelligence and dynamic system optimization, with a focus on developing advanced neural network algorithms for solving time-varying nonlinear equations. His major contributions include pioneering two neural dynamics approaches that efficiently compute systems of time-varying nonlinear equations, as detailed in his highly cited 2020 paper (32 citations). Huang has significantly advanced the field of adaptive gradient neural networks, introducing an accelerated approach with a hybrid state-triggered discretization (2024, 14 citations) to solve time-dependent linear equations. His work on noise-suppressing algorithms, such as the modified Newton-Raphson iteration for Lyapunov equations (2020, 22 citations) and the modified Newton integration algorithm for robotics (2021, 18 citations), demonstrates exceptional impact in engineering applications. Huang's research has garnered over 138 citations, reflecting its influence in robotics, control systems, and numerical optimization. Notably, his proportional-integral iterative algorithm for time-variant quadratic programming (2022, 13 citations) showcases his ability to address complex, real-world optimization challenges with noise tolerance and robustness.
Research Focus
Key Achievements
Top Papers
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- 2An Adaptive Gradient Neural Network to Solve Dynamic Linear Matrix Equations27 citations · 2021
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