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Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems with Unknown Nonaffine Dead-Zone Input Journal article
IEEE Transactions on Neural Networks and Learning Systems, 2019,Volume: 30,Issue: 1,Page: 295-305
Authors:  Liu Y.-J.;  Li S.;  Tong S.;  Chen C.L.P.
Favorite |  | TC[WOS]:32 TC[Scopus]:33 | Submit date:2019/02/11
Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning  
Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems Journal article
IEEE Transactions on Neural Networks and Learning Systems, 2015,Volume: 26,Issue: 1,Page: 165
Authors:  Liu Y.-J.;  Tang L.;  Tong S.;  Chen C.L.P.;  Li D.-J.
Favorite |  | TC[WOS]:156 TC[Scopus]:166 | Submit date:2018/10/30
Adaptive control  discrete-time systems  online approximators  reinforcement learning (RL)  uncertain nonlinear systems.