A simplified LVI-based primal-dual neural network for repetitive motion planning of PA10 robot manipulator starting from different initial states. - Yunong Zhang; Zhiguo Tan; Zhi Yang 004; Xuanjiao Lv; Ke Chen 004 (IJCNN, 2008)

Repetitive motion of redundant robots planned by three kinds of recurrent neural networks and illustrated with a four-link planar manipulator's straight-line example. - Yunong Zhang; Zhiguo Tan; Ke Chen 004; Zhi Yang 004; Xuanjiao Lv (Robotics Auton. Syst., 2009)

Adaptive traffic sampling for P2P botnet detection. - Jie He 002; Yuexiang Yang; Xiaolei Wang 003; Zhiguo Tan (Int. J. Netw. Manag., 2017)

Wsbp function activated Zhang dynamic with finite-time convergence applied to Lyapunov equation. - Xuanjiao Lv; Lin Xiao 002; Zhiguo Tan; Zhi Yang 004 (Neurocomputing, 2018)

Robustness Analysis and Robotic Application of Combined Function Activated RNN for Time-Varying Matrix Pseudo Inversion. - Zhiguo Tan; Yueming Hu 002; Lin Xiao 002; Ke Chen 004 (IEEE Access, 2019)

A novel recurrent neural network and its finite-time solution to time-varying complex matrix inversion. - Lin Xiao 002; Yongsheng Zhang; Kenli Li 001; Bolin Liao; Zhiguo Tan (Neurocomputing, 2019)

Improved Zhang neural network with finite-time convergence for time-varying linear system of equations solving. - Xuanjiao Lv; Lin Xiao 002; Zhiguo Tan (Inf. Process. Lett., 2019)

Nonlinear gradient neural network for solving system of linear equations. - Lin Xiao 002; Kenli Li 001; Zhiguo Tan; Zhijun Zhang 003; Bolin Liao; Ke Chen 004; Long Jin 001; Shuai Li 002 (Inf. Process. Lett., 2019)

Improved Gradient Neural Networks for Solving Moore-Penrose Inverse of Full-Rank Matrix. - Xuanjiao Lv; Lin Xiao 002; Zhiguo Tan; Zhi Yang 004; Junying Yuan (Neural Process. Lett., 2019)

A Variable-Gain Finite-Time Convergent Recurrent Neural Network for Time-Variant Quadratic Programming With Unknown Noises Endured. - Weibing Li; Zhizhuo Su; Zhiguo Tan (IEEE Trans. Ind. Informatics, 2019)

Zeroing Neural Dynamics for Control Design: Comprehensive Analysis on Stability, Robustness, and Convergence Speed. - Lin Xiao 002; Shuai Li 002; Faa-Jeng Lin; Zhiguo Tan; Ameer Hamza Khan (IEEE Trans. Ind. Informatics, 2019)

On the investigation of activation functions in gradient neural network for online solving linear matrix equation. - Zhiguo Tan; Yueming Hu 002; Ke Chen 004 (Neurocomputing, 2020)

Design and Application of A Robust Zeroing Neural Network to Kinematical Resolution of Redundant Manipulators Under Various External Disturbances. - Lin Xiao 002; Lei Jia; Jianhua Dai; Zhiguo Tan (Neurocomputing, 2020)

Noise-Tolerant and Finite-Time Convergent ZNN Models for Dynamic Matrix Moore-Penrose Inversion. - Zhiguo Tan; Lin Xiao 002; Siyuan Chen 006; Xuanjiao Lv (IEEE Trans. Ind. Informatics, 2020)

New Varying-Parameter ZNN Models With Finite-Time Convergence and Noise Suppression for Time-Varying Matrix Moore-Penrose Inversion. - Zhiguo Tan; Weibing Li; Lin Xiao 002; Yueming Hu 002 (IEEE Trans. Neural Networks Learn. Syst., 2020)

Robustness Analysis of a Fixed-Time Convergent GNN for Online Solving Sylvester Equation and Its Application to Robot Path Tracking. - Zhiguo Tan; Zhenlun Yang (IEEE Access, 2023)

Nonlinear function activated GNN versus ZNN for online solution of general linear matrix equations. - Zhiguo Tan; Haishan Chen (J. Frankl. Inst., 2023)

A Review of Deep Learning-Based LiDAR and Camera Extrinsic Calibration. - Zhiguo Tan; Xing Zhang; Shuhua Teng; Ling Wang; Feng Gao (Sensors, 2024)

Finite-time convergent gradient-zeroing neurodynamic system for solving temporally-variant linear simultaneous equation. - Zhiguo Tan; Yunong Zhang (Appl. Soft Comput., 2025)