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Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning Journal article
Transportation Research Part C: Emerging Technologies, 2021,Volume: 125
Authors:  Li,Zhenning;  Yu,Hao;  Zhang,Guohui;  Dong,Shangjia;  Xu,Cheng Zhong
Favorite |  | TC[WOS]:0 TC[Scopus]:1 | Submit date:2021/05/31
Adaptive traffic signal control  Deep learning  Knowledge sharing  Multi-agent reinforcement learning  Transportation network  
Robust Actor-Critic Learning for Continuous-Time Nonlinear Systems with Unmodeled Dynamics Journal article
IEEE Transactions on Fuzzy Systems, 2021
Authors:  Yang,Yongliang;  Gao,Weinan;  Modares,Hamidreza;  Xu,Cheng Zhong
Favorite |  | TC[WOS]:0 TC[Scopus]:0 | Submit date:2021/05/31
Fuzzy logic  Heuristic algorithms  input-to-state stability  Nonlinear dynamical systems  Optimal control  optimal control  Power system dynamics  robust actor-critic learning  Stability criteria  Uncertainty  unmodeled dynamics  
Adaptive neural tracking control for automotive engine idle speed regulation using extreme learning machine Journal article
Neural Computing and Applications, 2020,Volume: 32,Issue: 18,Page: 14399-14409
Authors:  Wong,Pak Kin;  Huang,Wei;  Vong,Chi Man;  Yang,Zhixin
Favorite |  | TC[WOS]:2 TC[Scopus]:2 | Submit date:2021/03/09
Adaptive neural control  Engine idle speed regulation  Extreme learning machine  Uncertain nonlinearity  
Initial-training-free online sequential extreme learning machine based adaptive engine air–fuel ratio control Journal article
International Journal of Machine Learning and Cybernetics, 2019,Volume: 10,Issue: 9,Page: 2245-2256
Authors:  Wong,Pak Kin;  Gao,Xiang Hui;  Wong,Ka In;  Vong,Chi Man;  Yang,Zhi Xin
Favorite |  | TC[WOS]:4 TC[Scopus]:4 | Submit date:2021/03/09
Adaptive control  Air–fuel ratio  Automotive engine  Online sequential extreme learning machine  
Broad Learning System for Control of Nonlinear Dynamic Systems Conference paper
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Authors:  Feng,Shuang;  Chen,C. L.Philip
Favorite |  | TC[WOS]:6 TC[Scopus]:6 | Submit date:2021/03/09
broad learning system  control  gradient descent  nonlinear system  
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]:32 | Submit date:2019/02/11
Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning  
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, Yan-Jun;  Li, Shu;  Tong, Shaocheng;  Chen, C. L. Philip
Favorite |  | TC[WOS]:32 TC[Scopus]:32 | Submit date:2019/01/17
Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning  
Optimized Multi-Agent Formation Control Based on an Identifier-Actor--Critic Reinforcement Learning Algorithm Journal article
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018,Volume: 26,Issue: 5,Page: 2719-2731
Authors:  Wen, Guoxing;  Chen, C. L. Philip;  Feng, Jun;  Zhou, Ning
View | Adobe PDF | Favorite |  | TC[WOS]:28 TC[Scopus]:31 | Submit date:2018/10/30
Fuzzy logic systems (FLSs)  identifier-actor-critic architecture  multi-agent formation  optimized formation control  reinforcement learning (RL)  
Optimized Multi-Agent Formation Control Based on an Identifier-Actor-Critic Reinforcement Learning Algorithm Journal article
IEEE Transactions on Fuzzy Systems, 2018,Volume: 26,Issue: 5,Page: 2719-2731
Authors:  Wen G.;  Chen C.L.P.;  Feng J.;  Zhou N.
Favorite |  | TC[WOS]:28 TC[Scopus]:31 | Submit date:2019/02/11
Fuzzy logic systems (FLSs)  identifier-actor-critic architecture  multi-agent formation  optimized formation control  reinforcement learning (RL)  
Adaptive air-fuel ratio control of dual-injection engines under biofuel blends using extreme learning machine Journal article
ENERGY CONVERSION AND MANAGEMENT, 2018,Volume: 165,Page: 66-75
Authors:  Wong, Ka In;  Wong, Pak Kin
Favorite |  | TC[WOS]:11 TC[Scopus]:12 | Submit date:2018/10/30
Biofuel  Dual-injection  Air fuel ratio control  Adaptive control  Extreme learning machine