Xuanjiao Lv
Papers
12
Total Citations
328
H-Index
10
About
Xuanjiao Lv is a researcher whose work spans the intersecting fields of neural network computation, robotics, and matrix mathematics, with particular expertise in applying recurrent neural network architectures to solve complex real-time optimization problems. A significant portion of Lv's career has focused on redundant robot manipulator control, where her contributions to repetitive motion planning have helped address the persistent joint angle drift problem that challenges multi-link robotic systems. Her early work, including studies of the PA10, PUMA560, and multi-link planar manipulators using LVI-based primal-dual neural networks, established foundational methodologies that collectively garnered over 200 citations. Lv later expanded her research toward dynamic matrix computation, developing improved gradient and Zhang neural network models capable of solving Moore-Penrose inverse problems in time-varying environments — an increasingly relevant challenge across engineering and data science applications. Her 2019 papers on noise-tolerant and finite-time convergent neural network models demonstrate a refined focus on robustness and computational efficiency. Across more than a decade of research, Lv has consistently advanced the practical deployment of neural computation techniques, making her body of work a valuable reference for researchers working at the intersection of intelligent systems, kinematics, and numerical methods.
Research Focus
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Top Papers
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