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Neuro-Optimal Trajectory Tracking With Value Iteration of Discrete-Time Nonlinear Dynamics 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 12
作者:  Wang, Ding;  Ha, Mingming;  Cheng, Long
收藏  |  浏览/下载：258/0  |  提交时间：2022/01/27
Trajectory  Heuristic algorithms  Convergence  Trajectory tracking  Stability criteria  Optimal control  Dynamic programming  Adaptive critic design  discrete-time nonlinear plants  neuro-optimal trajectory tracking  uniformly ultimately bounded stability  value iteration  
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：198/0  |  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)  
Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 1, 页码: 295-305
作者:  Liu, Yan-Jun;  Li, Shu;  Tong, Shaocheng;  Chen, C. L. Philip
收藏  |  浏览/下载：309/0  |  提交时间：2019/07/12
Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning  
Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 卷号: 25, 期号: 3, 页码: 621-634
作者:  Liu, Derong;  Wei, Qinglai
Adobe PDF(2635Kb)  |  收藏  |  浏览/下载：207/83  |  提交时间：2015/08/12
Adaptive Critic Designs  Adaptive Dynamic Programming (Adp)  Approximate Dynamic Programming  Discrete-time Policy Iteration  Neural Networks  Neurodynamic Programming  Nonlinear Systems  Optimal Control  Reinforcement Learning