咨询与建议

限定检索结果

文献类型

  • 745 篇 会议
  • 269 篇 期刊文献
  • 4 册 图书

馆藏范围

  • 1,018 篇 电子文献
  • 1 种 纸本馆藏

日期分布

学科分类号

  • 711 篇 工学
    • 520 篇 计算机科学与技术...
    • 380 篇 电气工程
    • 278 篇 控制科学与工程
    • 153 篇 软件工程
    • 79 篇 信息与通信工程
    • 40 篇 交通运输工程
    • 23 篇 仪器科学与技术
    • 20 篇 机械工程
    • 9 篇 生物工程
    • 8 篇 电子科学与技术(可...
    • 7 篇 力学(可授工学、理...
    • 7 篇 土木工程
    • 6 篇 动力工程及工程热...
    • 6 篇 石油与天然气工程
    • 4 篇 生物医学工程(可授...
    • 3 篇 材料科学与工程(可...
    • 3 篇 化学工程与技术
    • 3 篇 航空宇航科学与技...
    • 3 篇 安全科学与工程
  • 118 篇 理学
    • 98 篇 数学
    • 32 篇 系统科学
    • 22 篇 统计学(可授理学、...
    • 10 篇 生物学
    • 8 篇 物理学
    • 4 篇 化学
  • 66 篇 管理学
    • 63 篇 管理科学与工程(可...
    • 14 篇 工商管理
    • 5 篇 图书情报与档案管...
  • 5 篇 经济学
    • 4 篇 应用经济学
  • 3 篇 法学
    • 3 篇 社会学
  • 2 篇 医学
  • 1 篇 教育学

主题

  • 311 篇 reinforcement le...
  • 215 篇 dynamic programm...
  • 206 篇 optimal control
  • 107 篇 adaptive dynamic...
  • 104 篇 adaptive dynamic...
  • 97 篇 learning
  • 88 篇 neural networks
  • 77 篇 heuristic algori...
  • 68 篇 reinforcement le...
  • 58 篇 learning (artifi...
  • 54 篇 nonlinear system...
  • 53 篇 convergence
  • 51 篇 control systems
  • 51 篇 mathematical mod...
  • 48 篇 approximate dyna...
  • 44 篇 approximation al...
  • 43 篇 equations
  • 42 篇 adaptive control
  • 41 篇 artificial neura...
  • 41 篇 cost function

机构

  • 41 篇 chinese acad sci...
  • 27 篇 univ rhode isl d...
  • 17 篇 tianjin univ sch...
  • 16 篇 univ sci & techn...
  • 16 篇 univ illinois de...
  • 15 篇 northeastern uni...
  • 14 篇 beijing normal u...
  • 13 篇 northeastern uni...
  • 13 篇 guangdong univ t...
  • 12 篇 northeastern uni...
  • 9 篇 natl univ def te...
  • 8 篇 ieee
  • 8 篇 univ chinese aca...
  • 7 篇 univ chinese aca...
  • 7 篇 cent south univ ...
  • 7 篇 southern univ sc...
  • 7 篇 beijing univ tec...
  • 6 篇 chinese acad sci...
  • 6 篇 missouri univ sc...
  • 5 篇 nanjing univ pos...

作者

  • 54 篇 liu derong
  • 37 篇 wei qinglai
  • 29 篇 he haibo
  • 22 篇 wang ding
  • 21 篇 xu xin
  • 19 篇 jiang zhong-ping
  • 17 篇 lewis frank l.
  • 17 篇 yang xiong
  • 17 篇 zhang huaguang
  • 17 篇 ni zhen
  • 16 篇 zhao bo
  • 15 篇 gao weinan
  • 14 篇 zhao dongbin
  • 13 篇 zhong xiangnan
  • 12 篇 si jennie
  • 11 篇 derong liu
  • 10 篇 jagannathan s.
  • 10 篇 dongbin zhao
  • 10 篇 song ruizhuo
  • 9 篇 abouheaf mohamme...

语言

  • 992 篇 英文
  • 20 篇 其他
  • 6 篇 中文
检索条件"任意字段=IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning"
1018 条 记 录,以下是431-440 订阅
排序:
Dual MPC with reinforcement learning
Dual MPC with Reinforcement Learning
收藏 引用
11th IFAC symposium on dynamics and Control of Process Systems including Biosystems
作者: Morinelly, Juan E. Ydstie, B. Erik Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA
An adaptive optimal control algorithm for system with uncertain dynamics is formulated under a reinforcement learning framework. An embedded exploratory component, is included explicitly in the objective function of a... 详细信息
来源: 评论
A New Discrete-Time Iterative adaptive dynamic programming Algorithm Based on Q-learning  12th
收藏 引用
12th International symposium on Neural Networks (ISNN)
作者: Wei, Qinglai Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China
In this paper, a novel Q-learning based policy iteration adaptive dynamic programming (ADP) algorithm is developed to solve the optimal control problems for discrete-time nonlinear systems. The idea is to use a policy... 详细信息
来源: 评论
A Novel Resilient Control Scheme for a Class of Markovian Jump Systems With Partially Unknown Information
收藏 引用
ieee TRANSACTIONS ON CYBERNETICS 2022年 第8期52卷 8191-8200页
作者: Zhang, Kun Su, Rong Zhang, Huaguang Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Northeastern Univ Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China
In the complex practical engineering systems, many interferences and attacking signals are inevitable in industrial applications. This article investigates the reinforcement learning (RL)-based resilient control algor... 详细信息
来源: 评论
adaptive dynamic programming Based Motion Control of Autonomous Underwater Vehicles  5
Adaptive Dynamic Programming Based Motion Control of Autonom...
收藏 引用
5th International Conference on Control, Decision and Information Technologies (CoDIT)
作者: Vibhute, Siddhant VJTI Dept Elect Engn Mumbai Maharashtra India
In this paper, adaptive dynamic programming (ADP) technique is utilized to achieve optimal motion control of Autonomous Underwater Vehicle (AUV) System. The paper proposes a model-free based method that takes into con... 详细信息
来源: 评论
Advances in reinforcement learning and their implications for intelligent control
Advances in reinforcement learning and their implications fo...
收藏 引用
Proceedings of the 5th ieee International symposium on Intelligent Control 1990
作者: Whitehead, Steven D. Sutton, Richard S. Ballard, Dana H. Dept of Comput Sci Univ of Rochester NY USA
The focus of this work is on control architectures that are based on reinforcement learning. A number of recent advances that have contributed to the viability of reinforcement learning approaches to intelligent contr... 详细信息
来源: 评论
adaptive dynamic programming-based State Quantized Networked Control System without Value and/or Policy Iterations
Adaptive Dynamic Programming-based State Quantized Networked...
收藏 引用
International Joint Conference on Neural Networks (IJCNN)
作者: Zhao, Qiming Xu, Hao Jagannathan, S. Missouri Univ S&T Dept Elect & Comp Engn Rolla MO 65409 USA
In this paper, the Bellman equation is used to solve the stochastic optimal control of unknown linear discrete-time system with communication imperfections including random delays, packet losses and quantization. A dy... 详细信息
来源: 评论
Automatically customizing a powered knee prosthesis with human in the loop using adaptive dynamic programming
Automatically customizing a powered knee prosthesis with hum...
收藏 引用
2017 International symposium on Wearable Robotics and Rehabilitation, WeRob 2017
作者: Wen, Yue Brandt, Andrea Si, Jennie Huang, He Helen NCSU UNC Department of Biomedical Engineering NC State University RaleighNC27695-7115 United States University of North Carolina Chapel HillNC27599 United States Department of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States
In this study, we validated a human-in-the-loop auto-tuner using machine learning to automatically customize powered knee prosthesis control parameters for an amputee subject in real time. The experimental powered kne... 详细信息
来源: 评论
Event-Triggered Optimal Neuro-Controller Design With reinforcement learning for Unknown Nonlinear Systems
收藏 引用
ieee TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2019年 第9期49卷 1866-1878页
作者: Yang, Xiong He, Haibo Liu, Derong Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China
This paper develops an optimal control scheme for continuous-time unknown nonlinear systems using the event-triggering mechanism. Different from designing controllers using the time-triggering mechanism, the event-tri... 详细信息
来源: 评论
adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure
收藏 引用
ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2018年 第6期29卷 2099-2111页
作者: Luo, Biao Liu, Derong Wu, Huai-Ning Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Beihang Univ Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China
reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time syste... 详细信息
来源: 评论
Geometric deep reinforcement learning for dynamic DAG scheduling
Geometric deep reinforcement learning for dynamic DAG schedu...
收藏 引用
ieee symposium Series on Computational Intelligence (ieee SSCI)
作者: Grinsztajn, Nathan Beaumont, Olivier Jeannot, Emmanuel Preux, Philippe Univ Lille CNRS UMR 9189 CRIStAL INRIA Lille France Inria Bordeaux Hiepacs Team Bordeaux France Inria Bordeaux TADaaM Team Bordeaux France
In practice, it is quite common to face combinatorial optimization problems which contain uncertainly along with non determinism and dynamicity. These three properties call for appropriate algorithms;reinforcement lea... 详细信息
来源: 评论