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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]:0 | Submit date:2021/05/31
Adaptive traffic signal control  Deep learning  Knowledge sharing  Multi-agent reinforcement learning  Transportation network  
Adversarial example generation with adaptive gradient search for single and ensemble deep neural network Journal article
Information Sciences, 2020,Volume: 528,Page: 147-167
Authors:  Xiao,Yatie;  Pun,Chi Man;  Liu,Bo
Favorite |  | TC[WOS]:4 TC[Scopus]:5 | Submit date:2021/03/11
Adaptive gradient  Adversarial attack  Deep neural networks  Perturbation  
Crafting adversarial example with adaptive root mean square gradient on deep neural networks Journal article
Neurocomputing, 2020,Volume: 389,Page: 179-195
Authors:  Xiao,Yatie;  Pun,Chi Man;  Liu,Bo
Favorite |  | TC[WOS]:1 TC[Scopus]:1 | Submit date:2021/03/11
Adaptive gradient  Adversarial example  Perturbation  Root mean square  
Generating Adversarial Perturbation with Root Mean Square Gradient Conference paper
Proceedings of AAAI Workshops, 2019., Honolulu, Hawaii, USA, 2019-1-28
Authors:  Xiao, Yatie;  Pun, Chi-Man;  Zhou, Jiezhe
Favorite |  | TC[WOS]:0 TC[Scopus]:0 | Submit date:2019/05/21
A general moving detection method using dual-target nonparametric background model Journal article
Knowledge-Based Systems, 2019,Volume: 164,Page: 85-95
Authors:  Zhong Z.;  Wen J.;  Zhang B.;  Xu Y.
Favorite |  | TC[WOS]:8 TC[Scopus]:8 | Submit date:2019/04/04
Background modeling  Moving detection  Video surveillance  
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]:27 TC[Scopus]:29 | Submit date:2019/01/17
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 Y.-J.;  Li S.;  Tong S.;  Chen C.L.P.
Favorite |  | TC[WOS]:27 TC[Scopus]:29 | Submit date:2019/02/11
Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning  
Fuzzy Neural Networks (FNNs) Training Algorithm with Dropout via Its Equivalent Fully Connected Fuzzy Inference Systems (F-CONFIS) Conference paper
2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018
Authors:  Wang,Jing;  Chen,Philip;  Ma,Zhenyuan;  Xiao,Zhenghong
Favorite |  | TC[WOS]:0 TC[Scopus]:1 | Submit date:2021/03/09
Adaptive Neural-Fuzzy Inference Systems(ANFIS)  Fuzzy Inference Systems  Fuzzy Neural Networks  Gradient Descent  Neural Networks  
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]:26 TC[Scopus]:29 | Submit date:2018/10/30
Fuzzy logic systems (FLSs)  identifier-actor-critic architecture  multi-agent formation  optimized formation control  reinforcement learning (RL)  
Shared Autoencoder Gaussian Process Latent Variable Model for Visual Classification Journal article
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018,Volume: 29,Issue: 9,Page: 4272-4286
Authors:  Li, Jinxing;  Zhang, Bob;  Zhang, David
Favorite |  | TC[WOS]:5 TC[Scopus]:9 | Submit date:2018/10/30
Autoencoder  discriminative  Gaussian process (GP)  kernel  latent variable model  multiview