Bolin Liao
Papers
31
Total Citations
1,617
H-Index
20
About
Bolin Liao is a prominent researcher whose work sits at the intersection of recurrent neural networks, computational mathematics, and robotics. His scholarship centers on developing intelligent neural network architectures capable of solving complex, real-time mathematical problems, with particular emphasis on time-varying linear systems, matrix equations, and optimization under noisy, perturbed conditions. Liao's most significant contributions include pioneering noise-tolerant and finite-time convergent recurrent neural network models — notably zeroing neural networks (ZNN) — that address practical engineering challenges where classical methods fall short. His 2017 work on nonlinear recurrent neural networks for time-varying linear matrix equations (181 citations) and his distributed cooperative motion generation framework for redundant robot manipulators (177 citations) have become foundational references in the field. He has further advanced predefined-time convergence strategies and varying-parameter neural dynamics, tackling quadratic minimization and programming problems with demonstrated robustness. Beyond theoretical contributions, Liao has consistently bridged neural computation with robotics applications, proposing novel redundancy resolution schemes and repetitive motion planning frameworks for manipulator systems. With multiple papers exceeding 100 citations and a body of work spanning nearly a decade, his research has meaningfully shaped how neural networks are applied to dynamic, real-world computational and robotic control challenges.
Research Focus
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