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Learning and Guaranteed Cost Control With Event-Based Adaptive Critic Implementation 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 12, 页码: 6004-6014
作者:  Wang, Ding;  Liu, Derong
收藏  |  浏览/下载：252/0  |  提交时间：2019/07/12
Adaptive dynamic programming  event-based design  guaranteed cost control  optimal control  self-learning technique  
Data-Driven Finite-Horizon Approximate Optimal Control for Discrete-Time Nonlinear Systems Using Iterative HDP Approach 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2018, 卷号: 48, 期号: 10, 页码: 2948-2961
作者:  Mu, Chaoxu;  Wang, Ding;  He, Haibo
收藏  |  浏览/下载：217/0  |  提交时间：2019/12/16
Adaptive Dynamic Programming (Adp)  Data-driven Control  Finite-horizon Optimal Control  Heuristic Dynamic Programming (Hdp)  Learning Control  Neural Networks  
Reinforcement learning for robust adaptive control of partially unknown nonlinear systems subject to unmatched uncertainties 期刊论文
INFORMATION SCIENCES, 2018, 卷号: 463, 页码: 307-322
作者:  Yang, Xiong;  He, Haibo;  Wei, Qinglai;  Luo, Biao
收藏  |  浏览/下载：192/0  |  提交时间：2018/10/10
Adaptive Dynamic Programming  Neural Networks  Optimal Control  Reinforcement Learning  Robust Control  Unmatched Uncertainty  
An adaptive critic approach to event-triggered robust control of nonlinear systems with unmatched uncertainties 期刊论文
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2018, 卷号: 28, 期号: 10, 页码: 3501-3519
作者:  Yang, Xiong;  He, Haibo;  Wei, Qinglai
收藏  |  浏览/下载：173/0  |  提交时间：2018/10/10
Adaptive Critic Approach  Event-triggered Control  Reinforcement Learning  Robust Control  Unmatched Uncertainty  
Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 6, 页码: 2099-2111
作者:  Luo, Biao;  Liu, Derong;  Wu, Huai-Ning
浏览  |  Adobe PDF(1045Kb)  |  收藏  |  浏览/下载：370/113  |  提交时间：2018/10/10
Adaptive Control  Adaptive Dynamic Programming  Constraints  Critic-only  Data-based  Optimal Control  Q-learning  
Incremental Adaptive Learning Vector Quantization for Character Recognition with Continuous Style Adaptation 期刊论文
COGNITIVE COMPUTATION, 2018, 卷号: 10, 期号: 2, 页码: 334-346
作者:  Shen, Yuan-Yuan;  Liu, Cheng-Lin
Adobe PDF(1575Kb)  |  收藏  |  浏览/下载：270/72  |  提交时间：2018/10/10
Continuous Incremental Adaptive Learning Vector Quantization  Style Transfer Mapping  Local Style Consistency  Active Learning  
Neural Network Learning and Robust Stabilization of Nonlinear Systems With Dynamic Uncertainties 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 4, 页码: 1342-1351
作者:  Wang, Ding;  Liu, Derong;  Mu, Chaoxu;  Zhang, Yun
收藏  |  浏览/下载：143/0  |  提交时间：2018/10/10
Adaptive Critic  Dynamical Uncertainty  Learning Systems  Neural Networks  Optimal Control  Robust Stabilization  
Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 4, 页码: 1226-1238
作者:  Wei, Qinglai;  Li, Benkai;  Song, Ruizhuo
浏览  |  Adobe PDF(2475Kb)  |  收藏  |  浏览/下载：378/122  |  提交时间：2017/02/23
Adaptive Critic Designs  Adaptive Dynamic Programming (Adp)  Approximate Dynamic Programming  Generalized Policy Iteration (Gpi)  Neural Networks  Neurodynamic Programming  Nonlinear Systems  Optimal Control  Reinforcement Learning  
Manifold Regularized Reinforcement Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 4, 页码: 932-943
作者:  Li, Hongliang;  Liu, Derong;  Wang, Ding
收藏  |  浏览/下载：175/0  |  提交时间：2018/10/10
Adaptive Dynamic Programming  Approximate Dynamic Programming  Approximate Policy Iteration (Api)  Manifold Regularization  Reinforcement Learning (Rl)  
Comprehensive comparison of online ADP algorithms for continuous-time optimal control 期刊论文
ARTIFICIAL INTELLIGENCE REVIEW, 2018, 卷号: 49, 期号: 4, 页码: 531-547
作者:  Zhu, Yuanheng;  Zhao, Dongbin
Adobe PDF(766Kb)  |  收藏  |  浏览/下载：406/180  |  提交时间：2017/09/13
Adaptive Dynamic Programming  Policy Iteration  Integral Reinforcement Learning  Experience Replay  Off-policy