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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：448/135  |  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  
Energy consumption prediction of office buildings based on echo state networks 期刊论文
NEUROCOMPUTING, 2016, 卷号: 216, 期号: n/a, 页码: 478-488
作者:  Shi, Guang;  Liu, Derong;  Wei, Qinglai
浏览  |  Adobe PDF(1405Kb)  |  收藏  |  浏览/下载：469/201  |  提交时间：2017/02/14
Energy Consumption  Time-series Prediction  Office Buildings  Echo State Networks  Reservoir Topologies  
Neuro-optimal tracking control for a class of discrete-time nonlinear systems via generalized value iteration adaptive dynamic programming approach 期刊论文
SOFT COMPUTING, 2016, 卷号: 20, 期号: 2, 页码: 697-706
作者:  Wei, Qinglai;  Liu, Derong;  Xu, Yancai;  Qinglai Wei
浏览  |  Adobe PDF(790Kb)  |  收藏  |  浏览/下载：486/178  |  提交时间：2016/06/14
Adaptive Dynamic Programming  Approximate Dynamic Programming  Adaptive Critic Designs  Optimal Control  Neural Networks  Nonlinear Systems  Reinforcement Learning  
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：407/177  |  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