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RNN for Perturbed Manipulability Optimization of Manipulators Based on a Distributed Scheme: A Game-Theoretic Perspective 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 12, 页码: 5116-5126
作者:  Zhang, Jiazheng;  Jin, Long;  Cheng, Long
收藏  |  浏览/下载：305/0  |  提交时间：2021/03/02
Optimization  Neural networks  Task analysis  Nash equilibrium  Manipulator dynamics  Distributed control  game theory  manipulability optimization  neural network  redundancy resolution  
Reinforcement Learning-Based Optimal Stabilization for Unknown Nonlinear Systems Subject to Inputs With Uncertain Constraints 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 10, 页码: 4330-4340
作者:  Zhao, Bo;  Liu, Derong;  Luo, Chaomin
收藏  |  浏览/下载：242/0  |  提交时间：2021/01/07
Nonlinear systems  Optimal control  Artificial neural networks  Actuators  Observers  Feedforward systems  Adaptive dynamic programming (ADP)  neural networks (NNs)  optimal control  reinforcement learning (RL)  uncertain input constraints  unknown nonlinear systems  
Complex-Valued Discrete-Time Neural Dynamics for Perturbed Time-Dependent Complex Quadratic Programming With Applications 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 9, 页码: 3555-3569
作者:  Qi, Yimeng;  Jin, Long;  Wang, Yaonan;  Xiao, Lin;  Zhang, Jiliang
收藏  |  浏览/下载：232/0  |  提交时间：2021/01/07
Computational modeling  Convergence  Mathematical model  Recurrent neural networks  Perturbation methods  Robots  Numerical models  Complex domain  discrete-time neural dynamics (DTND)  quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS)  quadratic programming (QP)  
Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 6, 页码: 2064-2076
作者:  Li, Haoran;  Zhang, Qichao;  Zhao, Dongbin
Adobe PDF(4274Kb)  |  收藏  |  浏览/下载：418/129  |  提交时间：2020/08/03
Robot sensing systems  Navigation  Entropy  Neural networks  Task analysis  Planning  Automatic exploration  deep reinforcement learning (DRL)  optimal decision  partial observation  
Composite Learning Enhanced Robot Impedance Control 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 3, 页码: 1052-1059
作者:  Sun, Tairen;  Peng, Liang;  Cheng, Long;  Hou, Zeng-Guang;  Pan, Yongping
收藏  |  浏览/下载：291/0  |  提交时间：2020/06/02
Impedance  Convergence  Robots  Stability criteria  Uncertainty  Parameter estimation  Adaptive control  composite adaptation  impedance control  learning control  parameter convergence  robot