中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共8条，第1-8条 帮助

限定条件        
已选(0)清除 条数/页：   排序方式：
Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2017, 卷号: 47, 期号: 10, 页码: 3341-3354
作者:  Luo, Biao;  Liu, Derong;  Wu, Huai-Ning;  Wang, Ding;  Lewis, Frank L.
浏览  |  Adobe PDF(3217Kb)  |  收藏  |  浏览/下载：564/204  |  提交时间：2016/11/09
Adaptive Control  Adaptive Dynamic Programming (Adp)  Data-based  Off-policy Learning  Optimal Control  Policy Gradient  
Mixed Iterative Adaptive Dynamic Programming for Optimal Battery Energy Control in Smart Residential Microgrids 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017, 卷号: 64, 期号: 5, 页码: 4110-4120
作者:  Wei, Qinglai;  Liu, Derong;  Lewis, Frank L.;  Liu, Yu;  Zhang, Jie
浏览  |  Adobe PDF(4866Kb)  |  收藏  |  浏览/下载：537/242  |  提交时间：2017/02/23
Adaptive Critic Designs  Adaptive Dynamic Programming (Adp)  Approximate Dynamic Programming  Mixed Iteration  Optimal Control  Policy Iteration  Smart Grid  Value Iteration  
Self-tuned local feedback gain based decentralized fault tolerant control for a class of large-scale nonlinear systems 期刊论文
NEUROCOMPUTING, 2017, 期号: 235, 页码: 147-156
作者:  Zhao, Bo;  Li, Yuanchun;  Liu, Derong
浏览  |  Adobe PDF(1373Kb)  |  收藏  |  浏览/下载：382/105  |  提交时间：2017/05/05
Large-scale Nonlinear Systems  Partial Loss Of Actuator Effectiveness  Decentralized Fault Tolerant Control  Self-tuned Local Feedback Gain  Neural Network  
Echo state network-based Q-learning method for optimal battery control of offices combined with renewable energy 期刊论文
IET CONTROL THEORY AND APPLICATIONS, 2017, 卷号: 11, 期号: 7, 页码: 915-922
作者:  Shi, Guang;  Liu, Derong;  Wei, Qinglai
浏览  |  Adobe PDF(3253Kb)  |  收藏  |  浏览/下载：399/124  |  提交时间：2017/02/23
Recurrent Neural Nets  Neurocontrollers  Learning (Artificial Intelligence)  Office Environment  Optimal Control  Solar Power  Energy Consumption  Time Series  Secondary Cells  Energy Management Systems  Function Approximation  Echo State Network-based Q-learning Method  Optimal Battery Control  Renewable Energy  Optimal Energy Management  Solar Energy  Energy Consumption  Energy Demand  Time Series  Real-time Electricity Rate  Periodic Functions  Q-function  Optimal Charging Strategy  Optimal Discharging Strategy  Optimal Idle Strategy  Numerical Analysis  
Observer based adaptive dynamic programming for fault tolerant control of a class of nonlinear systems 期刊论文
INFORMATION SCIENCES, 2017, 期号: 384, 页码: 21-33
作者:  Zhao, Bo;  Liu, Derong;  Li, Yuanchun
浏览  |  Adobe PDF(1581Kb)  |  收藏  |  浏览/下载：355/131  |  提交时间：2017/05/05
Adaptive Dynamic Programming  Fault Tolerant Control  Policy Iteration  Nonlinear Systems  Fault Observer  Neural Network  
Data-Based Adaptive Critic Designs for Nonlinear Robust Optimal Control With Uncertain Dynamics 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2016, 卷号: 46, 期号: 11, 页码: 1544-1555
作者:  Wang, Ding;  Liu, Derong;  Zhang, Qichao;  Zhao, Dongbin
浏览  |  Adobe PDF(1082Kb)  |  收藏  |  浏览/下载：427/193  |  提交时间：2017/02/14
Adaptive Critic Designs  Adaptive Dynamic Programming  Intelligent Control  Neural Networks  Policy Iteration  Robust Optimal Control  System Identification  Uncertain Nonlinear Systems  
Model-Free Optimal Tracking Control via Critic-Only Q-Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 卷号: 27, 期号: 10, 页码: 2134-2144
作者:  Luo, Biao;  Liu, Derong;  Huang, Tingwen;  Wang, Ding;  Luo,Biao
浏览  |  Adobe PDF(1521Kb)  |  收藏  |  浏览/下载：565/283  |  提交时间：2016/10/24
Critic-only Q-learning (Coql)  Model-free  Nonaffine Nonlinear Systems  Optimal Tracking Control  
Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2016, 卷号: 46, 期号: 3, 页码: 840-853
作者:  Wei, Qinglai;  Liu, Derong;  Lin, Hanquan;  Derong Liu
浏览  |  Adobe PDF(2015Kb)  |  收藏  |  浏览/下载：368/161  |  提交时间：2016/06/14
Adaptive Critic Designs  Adaptive Dynamic Programming (Adp)  Approximate Dynamic Programming  Neural Networks  Neuro-dynamic Programming  Optimal Control  Reinforcement Learning  Value Iteration