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Orientation Tracking Incorporated Multicriteria Control for Redundant Manipulators With Dynamic Neural Network 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2024, 卷号: 71, 期号: 4, 页码: 3801-3810
作者:  Liu, Mei;  Shang, Mingsheng
收藏  |  浏览/下载：198/0  |  提交时间：2024/01/17
Computational complexity  dynamic neural network (DNN)  kinematic control  multicriteria control scheme  orientation tracking  
Neural dynamics solver for time-dependent infinity-norm optimization based on ACP framework with robot application 期刊论文
NEUROCOMPUTING, 2024, 卷号: 567, 页码: 9
作者:  Ma, Dexiu;  Liu, Mei;  Shang, Mingsheng
收藏  |  浏览/下载：8/0  |  提交时间：2024/02/23
Neural dynamics (ND)  ACP framework  Time-dependent infinity-norm optimization (TDINO)  Activation functions  
Data-Driven Remote Center of Cyclic Motion Control for Redundant Robots With Rod-Shaped End-Effector 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2024, 页码: 9
作者:  Liu, Mei;  Liu, Kun;  Zhu, Puchen;  Zhang, Guoqian;  Ma, Xin;  Shang, Mingsheng
收藏  |  浏览/下载：5/0  |  提交时间：2024/04/07
Robots  End effectors  Robot kinematics  Kinematics  Recurrent neural networks  Jacobian matrices  Task analysis  Data driven  end-effector  recurrent neural network (RNN)  redundant robot  remote center of motion (RCM)  
Neural Solution to Dynamic Overdetermined System With Applications to Data Fitting and Parameters Estimation 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 页码: 12
作者:  Liu, Mei;  Liufu, Ying;  Lu, Huiyan;  Shang, Mingsheng
收藏  |  浏览/下载：12/0  |  提交时间：2023/12/25
Artificial neural networks  Mathematical models  Real-time systems  Heuristic algorithms  Convergence  Computational modeling  Time-varying systems  Convergence property  dynamic overdetermined system  integral neural solution (INS)  noise-suppression ability  zeroing neural network (ZNN)  
A noise-suppressing discrete-time neural dynamics model for solving time-dependent multi-linear M-tensor equation q 期刊论文
NEUROCOMPUTING, 2023, 卷号: 520, 页码: 240-249
作者:  Liu, Mei;  Wu, Huanmei;  Shang, Mingsheng
收藏  |  浏览/下载：52/0  |  提交时间：2023/02/07
Multi -linear M -tensor equation  Time dependence  Noise -suppressing discrete -time neural  dynamics (NSDTND)  
On RNN-Based k-WTA Models With Time-Dependent Inputs 期刊论文
IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2022, 卷号: 9, 期号: 11, 页码: 2034-2036
作者:  Liu, Mei;  Shang, Mingsheng
收藏  |  浏览/下载：46/0  |  提交时间：2022/12/26
Computational Neural Dynamics Model for Time-Variant Constrained Nonlinear Optimization Applied to Winner-Take-All Operation 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 卷号: 18, 期号: 9, 页码: 5936-5948
作者:  Liu, Mei;  Zhang, Xiaoyan;  Shang, Mingsheng
收藏  |  浏览/下载：66/0  |  提交时间：2022/08/22
Optimization  Mathematical models  Computational modeling  Neural networks  Numerical models  Informatics  Vehicle dynamics  Equality and inequality constraints  noise-suppressing discrete-time neural dynamics (NSDTND) model  time-variant nonlinear optimization  
Gradient-Based Differential kWTA Network With Application to Competitive Coordination of Multiple Robots 期刊论文
IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2022, 卷号: 9, 期号: 8, 页码: 1452-1463
作者:  Liu, Mei;  Zhang, Xiaoyan;  Shang, Mingsheng;  Jin, Long
收藏  |  浏览/下载：55/0  |  提交时间：2022/10/14
Consensus filter  k-winners-take-all (kWTA)  multi-robot coordination  noise resistance  
Real-time cooperative kinematic control for multiple robots in distributed scenarios with dynamic neural networks 期刊论文
NEUROCOMPUTING, 2022, 卷号: 491, 页码: 621-632
作者:  Liu, Mei;  Zhang, Jiazheng;  Shang, Mingsheng
收藏  |  浏览/下载：64/0  |  提交时间：2022/08/22
Multiple redundant manipulators  Cooperative control  Motion planning  Dynamic neural network  
Dynamic Neural Network for Bicriteria Weighted Control of Robot Manipulators 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 14
作者:  Liu, Mei;  He, Li;  Shang, Mingsheng
收藏  |  浏览/下载：53/0  |  提交时间：2022/08/22
Manipulators  Robots  Indexes  Mathematical models  Manipulator dynamics  Optimization  Kinematics  Bicriteria weighted (BCW) scheme  dynamic neural network (DNN)  quadratic programming (QP) problem  robustness