Papers
16
Total Citations
316
H-Index
9
About
Jinjin Guo is a prominent researcher specializing in zeroing neural dynamics, discrete-time optimization, and intelligent control systems for robotic manipulators. His work centers on developing and advancing zeroing neurodynamic (ZN) algorithms to solve complex real-time optimization problems, including future equality-constrained nonlinear optimization, quadratic programming, and multi-constraint systems — challenges characterized by time-varying, unknown future information that traditional methods struggle to address effectively. Among his most significant contributions is the formulation of general Zhang et al. discretization (ZeaD) frameworks and explicit linear multi-step methods, which provide robust, noise-tolerant algorithms for discrete-time neural network implementation. His 2020 paper on discrete-time advanced zeroing neurodynamic algorithms has garnered 54 citations, reflecting its foundational impact on the field. Guo has also made notable strides in robotic control, designing novel neural network models that simultaneously manage joint-angle drift, kinetic energy dissipation, and end-effector precision in manipulator systems. His 2024 authored book on Zhang Time Discretization formulas consolidates much of this research into a systematic reference, underscoring his role in shaping this discipline. With a cumulative citation count exceeding 290 across his leading works, Guo's research has meaningfully advanced both the theoretical foundations and practical applications of neurodynamics-based computational intelligence.
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- 10Zhang Time Discretization (ZTD) Formulas and Applications9 citations · 2024