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NVIF: Neighboring Variational Information Flow for Cooperative Large-Scale Multiagent Reinforcement Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 页码: 13
作者:  Chai, Jiajun;  Zhu, Yuanheng;  Zhao, Dongbin
Adobe PDF(2469Kb)  |  收藏  |  浏览/下载：51/0  |  提交时间：2023/11/16
Large-scale multiagent  neighboring communication  reinforcement learning (RL)  variational information flow  
Data Generation Feedback Relearning Control for Unmodeled Nonlinear Systems 期刊论文
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2023, 页码: 12
作者:  Zhang, Yong;  Mu, Chaoxu;  Zhao, Dongbin
收藏  |  浏览/下载：91/0  |  提交时间：2023/11/16
Data models  Real-time systems  Heuristic algorithms  Mathematical models  Adaptation models  Approximation algorithms  Cost function  Data generation model  feedback relearning control  delayed neural network  reinforcement learning  unmodeled nonlinear system  
A Self-Attention-Based Deep Reinforcement Learning Approach for AGV Dispatching Systems 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 12
作者:  Wei, Qinglai;  Yan, Yutian;  Zhang, Jie;  Xiao, Jun;  Wang, Cong
收藏  |  浏览/下载：228/0  |  提交时间：2023/01/09
Automated guided vehicle (AGV) dispatching  deep learning  reinforcement learning (RL)  self-attention  
AHDet: A dynamic coarse-to-fine gaze strategy for active object detection 期刊论文
NEUROCOMPUTING, 2022, 卷号: 491, 页码: 522-532
作者:  Xu, Nuo;  Huo, Chunlei;  Zhang, Xin;  Pan, Chunhong
Adobe PDF(2664Kb)  |  收藏  |  浏览/下载：318/61  |  提交时间：2022/09/19
Object detection  Active object detection  Deep reinforcement learning  Convolutional neural networks  
Hierarchical Multihop Reasoning on Knowledge Graphs 期刊论文
IEEE INTELLIGENT SYSTEMS, 2022, 卷号: 37, 期号: 1, 页码: 71-78
作者:  Wang, Zikang;  Li, Linjing;  Zeng, Daniel Dajun
Adobe PDF(1656Kb)  |  收藏  |  浏览/下载：330/82  |  提交时间：2022/07/25
Event-Triggered Near-Optimal Control for Unknown Discrete-Time Nonlinear Systems Using Parallel Control 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2022, 页码: 15
作者:  Lu, Jingwei;  Wei, Qinglai;  Zhou, Tianmin;  Wang, Ziyang;  Wang, Fei-Yue
收藏  |  浏览/下载：224/0  |  提交时间：2022/07/25
Nonlinear systems  Optimal control  Control systems  Performance analysis  Stability criteria  Iterative algorithms  Heuristic algorithms  Adaptive dynamic programming (ADP)  event-triggered control  near-optimal control  parallel control  reinforcement learning (RL)  unknown nonlinear systems  
A Novel Parallel Control Method for Continuous-Time Linear Output Regulation With Disturbances 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 页码: 11
作者:  Wei, Qinglai;  Li, Hongyang;  Wang, Fei-Yue
Adobe PDF(1665Kb)  |  收藏  |  浏览/下载：257/41  |  提交时间：2022/01/27
Regulators  Regulation  Control systems  Stability analysis  Asymptotic stability  Eigenvalues and eigenfunctions  Control theory  Continuous-time linear systems  output regulation  parallel control theory  parallel regulators  parallel systems  
Unsupervised Video Summarization via Relation-Aware Assignment Learning 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 3203-3214
作者:  Gao, Junyu;  Yang, Xiaoshan;  Zhang, Yingying;  Xu, Changsheng
Adobe PDF(3649Kb)  |  收藏  |  浏览/下载：328/64  |  提交时间：2021/11/03
Feature extraction  Training  Optimization  Semantics  Recurrent neural networks  Task analysis  Graph neural network  unsupervised learning  video summarization  
Learning Control for Air Conditioning Systems via Human Expressions 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2021, 卷号: 68, 期号: 8, 页码: 7662-7671
作者:  Wei, Qinglai;  Li, Tao;  Liu, Derong
Adobe PDF(1354Kb)  |  收藏  |  浏览/下载：234/6  |  提交时间：2021/06/15
Adaptive dynamic programming  air conditioning control  deep learning (DL)  deep Q-network (DQN)  human expressions  optimal control  reinforcement learning (RL)  Q-learning  
SRGCN: Graph-based multi-hop reasoning on knowledge graphs 期刊论文
Neurocomputing, 2021, 卷号: 454, 期号: 454, 页码: 280-290
作者:  Wang, Zikang;  Li, Linjing;  Zeng, Daniel
Adobe PDF(1540Kb)  |  收藏  |  浏览/下载：219/62  |  提交时间：2021/05/31
knowledge graph  multi-hop reasoning  graph convolutional network