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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：442/133  |  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  
Online reinforcement learning control by Bayesian inference 期刊论文
IET CONTROL THEORY AND APPLICATIONS, 2016, 卷号: 10, 期号: 12, 页码: 1331-1338
作者:  Xia, Zhongpu;  Zhao, Dongbin;  Dongbin Zhao
浏览  |  Adobe PDF(1559Kb)  |  收藏  |  浏览/下载：369/119  |  提交时间：2016/06/15
Learning Systems  Bayes Methods  Gaussian Processes  Optimal Control  Online Reinforcement Learning Control  Bayesian Inference  Self-learning Control  Probability  Action Value Function  Gaussian Process  Bayesian-state-action-reward-state-action Algorithm  
Adaptive neuro-fuzzy sliding mode control guidance law with impact angle constraint 期刊论文
IET CONTROL THEORY AND APPLICATIONS, 2015, 卷号: 9, 期号: 14, 页码: 2115-2123
作者:  Li, Qingchun;  Zhang, Wensheng;  Han, Gang;  Yang, Yehui
浏览  |  Adobe PDF(1024Kb)  |  收藏  |  浏览/下载：360/111  |  提交时间：2015/10/13