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121-130 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：

Effective Elastic Scaling of Deep learning Workloads 28

Effective Elastic Scaling of Deep Learning Workloads

引用

28th ieee international symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (ieee MASCOTS)

作者： Saxena, Vaibhav Jayaram, K. R. Basu, Saurav Sabharwal, Yogish Verma, Ashish IBM Res Delhi India Microsoft Hyderabad India

ISBN: (纸本)9781728192383

We examine the elastic scaling of Deep learning (DL) jobs and propose a novel resource allocation strategy for DL training jobs, resulting in improved job run time performance as well as increased cluster utilization. We begin by analyzing DL workloads and exploit the fact that DL jobs can be run with a range of batch sizes without affecting their final accuracy. We formulate an optimization problem that explores a dynamic batch size allocation to individual DL jobs based on their scaling efficiency, when running on multiple nodes. We design a fast dynamic programming based optimizer to solve this problem in real-time to determine jobs that can be scaled up/down, and use this optimizer in an autoscaler to dynamically change the allocated resources and batch sizes of individual DL jobs. We demonstrate empirically that our elastic scaling algorithm can complete up to approximate to 2x as many jobs as compared to a strong baseline algorithm that also scales the number of GPUs but does not change the batch size, with average completion times up to approximate to 10x faster.

关键词： elasticity deep learning variable batch size

来源：评论

学校读者我要写书评

暂无评论

Revisiting Maximum Entropy Inverse reinforcement learning: New Perspectives and Algorithm

Revisiting Maximum Entropy Inverse Reinforcement Learning: N...

引用

ieee symposium Series on Computational Intelligence (ieee SSCI)

作者： Snoswell, Aaron J. Singh, Surya P. N. Ye, Nan Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia Intuit Surg Sunnyvale CA USA Univ Queensland Sch Math & Phys Brisbane Qld Australia

ISBN: (纸本)9781728125473

We provide new perspectives and inference algorithms for Maximum Entropy (MaxEnt) Inverse reinforcement learning (IRL), which provides a principled method to find a most non-committal reward function consistent with given expert demonstrations, among many consistent reward functions. We first present a generalized MaxEnt formulation based on minimizing a KL-divergence instead of maximizing an entropy. This improves the previous heuristic derivation of the MaxEnt IRL model (for stochastic MDPs), allows a unified view of MaxEnt IRL and Relative Entropy IRL, and leads to a model-free learning algorithm for the MaxEnt IRL model. Second, a careful review of existing inference algorithms and implementations showed that they approximately compute the marginals required for learning the model. We provide examples to illustrate this, and present an efficient and exact inference algorithm. Our algorithm can handle variable length demonstrations;in addition, while a basic version takes time quadratic in the maximum demonstration length an improved version of this algorithm reduces this to linear using a padding trick. Experiments show that our exact algorithm improves reward learning as compared to the approximate ones. Furthermore, our algorithm scales up to a large, real-world dataset involving driver behaviour forecasting. We provide an optimized implementation compatible with the OpenAl Gym interface. Our new insight and algorithms could possibly lead to further interest and exploration of the original MaxEnt IRL model.

关键词： Inverse reinforcement learning reinforcement learning Maximum Entropy dynamic programming Algorithms

来源：评论

学校读者我要写书评

暂无评论

2007 ieee ADPRL international Program Committee Members

2007 IEEE ADPRL International Program Committee Members

引用

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

Provides a listing of current committee members.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A reinforcement learning-based Bi-Objective Routing Algorithm for Energy Harvesting Mobile Ad-hoc Networks 7

A Reinforcement Learning-based Bi-Objective Routing Algorith...

引用

7th international symposium on Telecommunications (IST)

作者： Maleki, Meisam Hakami, Vesal Dehghan, Mehdi Amirkabir Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781479953592

dynamic topology, lack of a fixed infrastructure and limited energy in mobile ad-hoc networks (MANETs) give rise to a challenging operational environment. MANET routing protocols should consider dynamic network changes (e.g., link qualities and nodes' residual energy) in such circumstances and be able to adapt to these changes to efficiently handle the traffic flows. In this paper, we present a bi-objective intelligent routing protocol that aims at reducing an expected long-run cost function composed of end-to-end delay and the path energy cost. We formulate the routing problem as a Markov decision process (MDP) which captures both the link state dynamics due to node mobility and energy state dynamics due to nodes' rechargeable energy sources. We propose a reinforcement learning-based algorithm to approximate the optimal routing policy in the absence of a priori knowledge of the system statistics. We compare the performance of the proposed scheme with that obtained from a value-iteration-based algorithm which assumes perfect statistics.

关键词： MANET routing reinforcement learning MDP end to end delay network life time

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Q-learning: a new approach for fuzzy dynamic programming

Fuzzy Q-learning: a new approach for fuzzy dynamic programmi...

引用

Proceedings of the 3rd ieee Conference on Fuzzy Systems. Part 3 (of 3)

作者： Berenji, Hamid R. NASA Ames Research Cent Mountain View United States

Fuzzy reinforcement learning (FRL) involves 'jump starting' reinforcement learning with fuzzy logic rules. By using FRL, prior domain knowledge, which may be very approximate and imprecise, can be expressed in terms of fuzzy rules and refined later through the learning process. In this paper, we develop a new algorithm called Fuzzy Q-learning (or FQ-learning) which extends Watkin's Q-learning method. It can be used for decision processes in which the goals and/or the constraints, but not necessarily the system under control, are fuzzy in nature. An example of a fuzzy constraint is: 'the weight of object A must not be substantially heavier than w' where w is a specified weight. Similarly, an example of a fuzzy goal is: 'the robot must be in the vicinity of door k'. We show that FQ-learning provides an alternative solution to this problem which is simpler than the Bellman-Zadeh's fuzzy dynamic programming approach. We apply the algorithm to a multistage decision making problem and a navigation task.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

A self-learning reactive navigation method for mobile robots

A self-learning reactive navigation method for mobile robots

引用

international Conference on Machine learning and Cybernetics

作者： Xu, X Wang, XN He, HG Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China

ISBN: (纸本)0780378652

This paper addresses the navigation problem of mobile robots in unknown environments, where global path planning methods cannot be applied. In such cases, reactive navigation controllers are commonly employed to deal with the uncertainties in motion planning and control. To realize the automatic design of reactive navigation controllers, a self-learning navigation method is proposed in this paper. The self-learning reactive navigation method is based on a Markov decision model of the navigation problem and uses reinforcement learning algorithms to optimize the action policies of mobile robots. Neural networks are employed to approximate value functions in continuous state spaces so that the self-learning navigation controller has good generalization ability and learning efficiency. Simulation results illustrate the effectiveness of the proposed method.

关键词： reinforcement learning mobile robots navigation dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Trajectory Tracking of Underactuated Sea Vessels With Uncertain dynamics: An Integral reinforcement learning Approach

Trajectory Tracking of Underactuated Sea Vessels With Uncert...

引用

ieee international Conference on Systems, Man, and Cybernetics (SMC)

作者： Abouheaf, Mohammed Gueaieb, Wail Miah, Md Suruz Spinello, Davide Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada Bradley Univ Dept Elect & Comp Engn Peoria IL USA Univ Ottawa Dept Mech Engn Ottawa ON Canada

ISBN: (纸本)9781728185262

Underactuated systems like sea vessels have degrees of motion that are insufficiently matched by a set of independent actuation forces. In addition, the underlying trajectory-tracking control problems grow in complexity in order to decide the optimal rudder and thrust control signals. This enforces several difficult-to-solve constraints that are associated with the error dynamical equations using classical optimal tracking and adaptive control approaches. An online machine learning mechanism based on integral reinforcement learning is proposed to find a solution for a class of nonlinear tracking problems with partial prior knowledge of the system dynamics. The actuation forces are decided using innovative forms of temporal difference equations relevant to the vessel's surge and angular velocities. The solution is implemented using an online value iteration process which is realized by employing means of the adaptive critics and gradient descent approaches. The adaptive learning mechanism exhibited well-functioning and interactive features in react to different desired reference-tracking scenarios.

关键词： approximate dynamic programming Integral reinforcement learning Adaptive Critics Underactuated Vessels

来源：评论

学校读者我要写书评

暂无评论

programming and reinforcement learning

Programming and Reinforcement Learning

引用

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

Welcome to ADPRL 2007 - the very first 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. You will enjoy an extraordinary technical program thanks to the ADPRL 2007 international Program Committee members who worked very hard to have all papers reviewed before the review deadline. We received a total of 65 submissions from various parts of the world. The final technical program consists of 49 papers among which 40 are oral session papers and 9 are poster session papers. There will be a keynote lecture delivered by Frank L. Lewis entitled “Adaptive dynamic programming for Robust Optimal Control Using Nonlinear Network learning Structures.”

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimal Tracking Control of the Boiler-turbine System Based on Adaptive dynamic programming

Optimal Tracking Control of the Boiler-turbine System Based ...

引用

international Joint Conference on Neural Networks (IJCNN)

作者： Liu, Yujia Gao, Aiguo Wei, Qinglai Chinese Acad Sci Inst Automat Beijing Peoples R China North China Elect Power Res Inst Co Ltd Beijing Peoples R China

ISBN: (纸本)9780738133669

To guarantee the efficient performance of the power plant, an adaptive tacking controller for the nonlinear boiler-turbine system based on offline policy iteration adaptive dynamic prorgamming (ADP) method is proposed in this paper. The optimal tracking controller is obtained through offline learning, which can maintain the characteristics of load changes in drum boiler-turbine type power plants. To implement the proposed method, neural networks (NNs) are used to construct the cost function and approximate optimal solution is achived. Then convergence of the method is analyzed. Simulation studies on the typical boiler-turbine system demonstrate that the proposed control strategy can achieve a satisfactory performance during a short period.

关键词： Integral reinforcement learning adaptive dynamic programming (ADP) boiler-turbine system policy iteration neural network

来源：评论

学校读者我要写书评

暂无评论

Incorporating approximate dynamic programming-Based Parameter Tuning into PD-type Virtual Inertia Control of DFIGs

Incorporating Approximate Dynamic Programming-Based Paramete...

引用

international Joint Conference on Neural Networks (IJCNN)

作者： Guo, Wentao Liu, Feng Si, Jennie Mei, Shengwei Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China Arizona State Univ Dept Elect Engn Tempe AZ 85287 USA

ISBN: (纸本)9781467361293;9781467361286

Doubly fed induction generators (DFIGs) are widely used in wind power generation. For controlling DFIGs to maintain network frequency within a safety range, the proportional-derivative (PD) type virtual inertia controllers (VIC) are used in the active power control of DFIGs. However, as is well known, wind power generation conditions change directly with wind conditions in nature. Such changes create great challenge for the VIC design and actually force the control designs to go beyond the traditional problem formulation of using explicit objective functions associated with specific optimality. Controller parameter tuning thus necessarily becomes a part of the controller design. In this paper, we propose an approximate dynamic programming (ADP) structure for online tuning of the PD type virtual inertia controller parameters. The proposed ADP structure naturally takes into account the PD control into design objective and provides the PD controller with online parameter tuning capability through learning. Design and implementation details of the proposed methodology, including neural network weight initialization, design of the reinforcement signal, data preprocessing, and a bound on the online tuned parameters are discussed in this paper. Simulation studies carried out on the Power System Computer Aided Design/ Electro Magnetic Transient in DC System (PSCAD/EMTDC) software are used to demonstrate the effectiveness and efficiency of the proposed ADP-based online VIC parameter tuning methodology.

关键词： approximate dynamic programming (ADP) Direct Heuristic dynamic programming (direct HDP) Doubly Fed Induction Generator (DFIG) Parameter Tuning Virtual Inertia Control (VIC)

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 << < 9 10 11 12 13 14 15 16 17 18 > >>

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：