检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

299 篇 会议
8 篇 期刊文献

馆藏范围

307 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

180 篇 工学
- 158 篇 计算机科学与技术...
- 56 篇 电气工程
- 48 篇 软件工程
- 47 篇 控制科学与工程
- 13 篇 信息与通信工程
- 10 篇 机械工程
- 6 篇 仪器科学与技术
- 4 篇 力学（可授工学、理...
- 4 篇 生物工程
- 3 篇 动力工程及工程热...
- 2 篇 交通运输工程
- 2 篇 核科学与技术
- 2 篇 生物医学工程（可授...
- 1 篇 建筑学
- 1 篇 化学工程与技术
- 1 篇 航空宇航科学与技...
- 1 篇 食品科学与工程（可...
40 篇 理学
- 35 篇 数学
- 9 篇 系统科学
- 8 篇 统计学（可授理学、...
- 4 篇 物理学
- 4 篇 生物学
- 1 篇 化学
- 1 篇 天文学
- 1 篇 大气科学
- 1 篇 地球物理学
- 1 篇 地质学
18 篇 管理学
- 17 篇 管理科学与工程(可...
- 7 篇 工商管理
4 篇 经济学
- 4 篇 应用经济学
1 篇 医学

主题

115 篇 dynamic programm...
76 篇 reinforcement le...
67 篇 learning
47 篇 optimal control
30 篇 neural networks
27 篇 control systems
21 篇 approximate dyna...
21 篇 approximation al...
20 篇 function approxi...
20 篇 equations
17 篇 convergence
16 篇 adaptive dynamic...
16 篇 state-space meth...
16 篇 heuristic algori...
14 篇 mathematical mod...
13 篇 stochastic proce...
12 篇 learning (artifi...
12 篇 adaptive control
12 篇 cost function
11 篇 algorithm design...

机构

5 篇 arizona state un...
4 篇 department of el...
4 篇 school of inform...
4 篇 department of in...
4 篇 univ sci & techn...
4 篇 chinese acad sci...
4 篇 department of el...
3 篇 princeton univ d...
3 篇 northeastern uni...
3 篇 national science...
3 篇 robotics institu...
3 篇 univ illinois de...
3 篇 univ utrecht dep...
2 篇 univ groningen i...
2 篇 sharif univ tech...
2 篇 univ texas autom...
2 篇 pengcheng labora...
2 篇 guangxi univ sch...
2 篇 chinese acad sci...
2 篇 cemagref lisc au...

作者

14 篇 liu derong
9 篇 wei qinglai
8 篇 si jennie
7 篇 xu xin
5 篇 derong liu
4 篇 lewis frank l.
4 篇 martin riedmille...
4 篇 huaguang zhang
4 篇 jennie si
4 篇 marco a. wiering
4 篇 xin xu
4 篇 zhang huaguang
4 篇 dongbin zhao
4 篇 lei yang
4 篇 powell warren b.
4 篇 riedmiller marti...
3 篇 hado van hasselt
3 篇 van hasselt hado
3 篇 jagannathan s.
3 篇 munos remi

语言

305 篇 英文
1 篇 其他
1 篇 中文

检索条件"任意字段=IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning"

共 307 条记录，以下是131-140 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Adaptive dynamic programming for optimal control of unknown nonlinear discrete-time systems

Adaptive dynamic programming for optimal control of unknown ...

引用

ieee symposium on Adaptive dynamic programming and reinforcement learning

作者： Liu, Derong Wang, Ding Zhao, Dongbin Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781424498888

An intelligent optimal control scheme for unknown nonlinear discrete-time systems with discount factor in the cost function is proposed in this paper. An iterative adaptive dynamic programming (ADP) algorithm via globalized dual heuristic programming (GDHP) technique is developed to obtain the optimal controller with convergence analysis. Three neural networks are used as parametric structures to facilitate the implementation of the iterative algorithm, which will approximate at each iteration the cost function, the optimal control law, and the unknown nonlinear system, respectively. Two simulation examples are provided to verify the effectiveness of the presented optimal control approach. © 2011 ieee.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

ieee SSCI 2011: symposium Series on Computational Intelligence - ADPRL 2011: 2011 ieee symposium on Adaptive dynamic programming and reinforcement learning

IEEE SSCI 2011: Symposium Series on Computational Intelligen...

引用

symposium Series on Computational Intelligence, ieee SSCI2011 - 2011 ieee symposium on Adaptive dynamic programming and reinforcement learning, ADPRL 2011

ISBN: (纸本)9781424498888

The proceedings contain 45 papers. The topics discussed include: active learning for personalizing treatment;active exploration by searching for experiments that falsify the computed control policy;optimistic planning for sparsely stochastic systems;adaptive sample collection using active learning for kernel-based approximate policy iteration;tree-based variable selection for dimensionality reduction of large-scale control systems;high-order local dynamic programming;safe reinforcement learning in high-risk tasks through policy improvement;agent self-assessment: determining policy quality without execution;reinforcement learning algorithms for solving classification problems;reinforcement learning in multidimensional continuous action spaces;grounding subgoals in information transitions;and directed exploration of policy space using support vector classifiers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

dynamic lead time promising

Dynamic lead time promising

引用

ieee symposium on Adaptive dynamic programming and reinforcement learning

作者： Reindorp, Matthew J. Fu, Michael C. Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands Robert H. Smith School of Business Institute for Systems Research University of Maryland United States

ISBN: (纸本)9781424498888

We consider a make-to-order business that serves customers in multiple priority classes. Orders from customers in higher classes bring greater revenue, but they expect shorter lead times than customers in lower classes. In making lead time promises, the firm must recognize preexisting order commitments, uncertainty over future demand from each class, and the possibility of supply chain disruptions. We model this scenario as a Markov decision problem and use reinforcement learning to determine the firm's lead time policy. In order to achieve tractability on large problems, we utilize a sequential decision-making approach that effectively allows us to eliminate one dimension from the state space of the system. Initial numerical results from the sequential dynamic approach suggest that the resulting policies more closely approximate optimal policies than static optimization approaches. © 2011 ieee.

关键词： reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

High-order local dynamic programming

High-order local dynamic programming

引用

作者： Tassa, Yuval Todorov, Emanuel Interdisciplinary Center for Neural Computation Hebrew University Jerusalem Israel Applied Mathematics and Computer Science and Engineering University of Washington Seattle United States

ISBN: (纸本)9781424498888

We describe a new local dynamic programming algorithm for solving stochastic continuous Optimal Control problems. We use cubature integration to both propagate the state distribution and perform the Bellman backup. The algorithm can approximate the local policy and cost-to-go with arbitrary function bases. We compare the classic quadratic cost-to-go/linear-feedback controller to a cubic cost-to-go/quadratic policy controller on a 10-dimensional simulated swimming robot, and find that the higher order approximation yields a more general policy with a larger basin of attraction. © 2011 ieee.

关键词： dynamic programming

来源：评论

学校读者我要写书评

暂无评论

A non-parametric approach to approximate dynamic programming

A non-parametric approach to approximate dynamic programming

引用

10th international Conference on Machine learning and Applications, ICMLA 2011

作者： Glaude, Hadrien Akrimi, Fadi Geist, Matthieu Pietquin, Olivier 57070 Metz France 2 rue Edouard Belin 57070 Metz France

ISBN: (纸本)9780769546070

approximate dynamic programming (ADP) is a machine learning method aiming at learning an optimal control policy for a dynamic and stochastic system from a logged set of observed interactions between the system and one or several non-optimal controlers. It defines a class of particular reinforcement learning (RL) algorithms which is a general paradigm for learning such a control policy from interactions. ADP addresses the problem of systems exhibiting a state space which is too large to be enumerated in the memory of a computer. Because of this, approximation schemes are used to generalize estimates over continuous state spaces. Nevertheless, RL still suffers from a lack of scalability to multidimensional continuous state spaces. In this paper, we propose the use of the Locally Weighted Projection Regression (LWPR) method to handle this scalability problem. We prove the efficacy of our approach on two standard benchmarks modified to exhibit larger state spaces. © 2011 ieee.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Optimal Control for a Class of Unknown Nonlinear Systems via the Iterative GDHP Algorithm

Optimal Control for a Class of Unknown Nonlinear Systems via...

引用

8th international symposium on Neural Networks

作者： Wang, Ding Liu, Derong Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9783642210891

Using the neural-network-based iterative adaptive dynamic programming (ADP) algorithm, an optimal control scheme for a class of unknown discrete-time nonlinear systems with discount factor in the cost function is proposed in this paper. The optimal controller is designed with convergence analysis in terms of cost function and control law. In order to implement the algorithm via globalized dual heuristic programming (CDHP) technique, a neural network is constructed first to identify the unknown nonlinear system, and then two other neural networks are used to approximate the cost function and the control law, respectively. An example is provided to verify the effectiveness of the present approach.

关键词： Adaptive critic designs adaptive dynamic programming approximate dynamic programming intelligent control neural networks optimal control reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

symposium on adaptive dynamic programming and reinforcement learning (ieee ADPRL 2011)

Symposium on adaptive dynamic programming and reinforcement ...

引用

ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

ADPRL 2011 is the third ieee international symposium on approximate dynamic programming and reinforcement learning. The area of approximate dynamic programming and reinforcement learning is a fusion of a number of research areas in engineering, mathematics, artificial intelligence, operations research, and systems and control theory. This symposium brings together researchers from different disciplines and will provide a remarkable opportunity for the academic and industrial community to address new challenges, share innovative yet practical solutions, and define promising future research directions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A new approach for power management in sensor node based on reinforcement learning

A new approach for power management in sensor node based on ...

引用

international symposium on Computer Networks and Distributed Systems

作者： Kianpisheh, Somayeh Charkari, Nasrolah Moghadam Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781424491544

Wireless sensor networks are composed of small nodes with limited battery life and computational ability. Energy reduction in these networks is an important issue to extend network lifetime. dynamic power management is a technique to conserve energy. DPM [1] uses dynamic programming to manage power in sensor nodes. This approach is model based and exploiting it in a multi hop scenario is difficult. In this paper, we propose RLPM which is based on reinforcement learning. It is model free and easily applicable in both single hop and multi hop scenario. Experiments show that RLPM behaves similar to DPM while it does not have those constraints of DPM. © 2011 ieee.

关键词： reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Dual Heuristic programming Based on Delta-Bar-Delta learning Rule

Adaptive Dual Heuristic Programming Based on Delta-Bar-Delta...

引用

8th international symposium on Neural Networks

作者： Wu, Jun Xu, Xin Lian, Chuanqiang Huang, Yan Natl Univ Def Technol Coll Mechatron & Automat Inst Automat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783642211102

Dual Heuristic programming (DHP) is a class of approximate dynamic programming methods using neural networks. Although there have been some successful applications of DHP, its performance and convergence are greatly influenced by the design of the step sizes in the critic module as well as the actor module. In this paper, a Delta-Bar-Delta learning rule is proposed for the DHP algorithm, which helps the two modules adjust learning rate individually and adaptively. Finally, the feasibility and effectiveness of the proposed method are illustrated in the learning control task of an inverted pendulum.

关键词： reinforcement learning adaptive critic design dual heuristic programming Delta-Bar-Delta neural networks

来源：评论

学校读者我要写书评

暂无评论

Optimization Control of Rectifier in HVDC System with ADHDP

Optimization Control of Rectifier in HVDC System with ADHDP

引用

8th international symposium on Neural Networks

作者： Song, Chunning Zhou, Xiaohua Lin, Xiaofeng Song, Shaojian Guangxi Univ Coll Elect Engn Guangxi Nanning 530004 Peoples R China

ISBN: (纸本)9783642211102

A novel nonlinear optimal controller for a rectifier in HVDC transmission system, using artificial neural networks, is presented in this paper. The action dependent heuristic dynamic programming(ADHDP), a member of the adaptive critic designs family is used for the design of the rectifier neurocontroller. This neurocontroller provides optimal control based on reinforcement learning and approximate dynamic programming(ADP). A series of simulations for a rectifier in dulble-ended unipolar HVDC system model with proposed neurocontroller and conventional PI controller were carried out in MAT-LAB/Simulink environment. Simulation results are provided to show that the proposed controller performs better than the conventional PI controller, the current of DC line in HVDC system with the proposed controller can quickly track with the changing of the reference current and prevent the occurrence of the current of DC line collapse when the large disturbances occur.

关键词： Optimal control rectifier HVDC transmission system approximate dynamic programming(ADP) action dependent heuristic dynamic programming (ADHDP)

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共31页 << < 10 11 12 13 14 15 16 17 18 19 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：