Papers
52
Total Citations
2,504
H-Index
27
About
Lin Xiao is a prominent researcher specializing in recurrent neural networks (RNNs), computational intelligence, and robotics, with particular expertise in developing neural network-based solutions for complex time-varying mathematical problems. His work sits at the intersection of applied mathematics, neural computation, and robotic control systems, making significant contributions to each domain. Xiao's most influential contributions center on designing novel RNN architectures capable of solving time-varying linear and nonlinear equations with finite-time convergence and robust noise tolerance — challenges that traditional approaches struggle to address effectively. His 2017 paper on nonlinear recurrent neural networks for time-varying linear matrix equations has garnered 181 citations, reflecting its foundational impact. Equally notable is his work on distributed cooperative motion generation for redundant robot manipulators (177 citations), demonstrating how neural frameworks can coordinate multi-robot systems under realistic communication constraints. Beyond theoretical modeling, Xiao has consistently bridged computation and physical application. His research on zeroing neural networks (ZNNs), complex-valued neural dynamics, and predefined-time matrix inversion has shaped modern approaches to real-time robotic control, including joint-drift correction and motion tracking. With multiple papers exceeding 100 citations published between 2017 and 2019 alone, Xiao's research trajectory reflects both prolific output and substantial, sustained influence within the intelligent robotics and neural computation communities.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7A New Performance Index for the Repetitive Motion of Mobile Manipulators118 citations · 2013
- 8
- 9
- 10