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

451 篇 会议
27 篇 期刊文献

馆藏范围

478 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

376 篇 工学
- 255 篇 计算机科学与技术...
- 233 篇 控制科学与工程
- 86 篇 电气工程
- 73 篇 软件工程
- 51 篇 机械工程
- 30 篇 石油与天然气工程
- 20 篇 生物工程
- 17 篇 信息与通信工程
- 15 篇 力学（可授工学、理...
- 12 篇 生物医学工程（可授...
- 9 篇 动力工程及工程热...
- 8 篇 电子科学与技术（可...
- 8 篇 交通运输工程
- 6 篇 材料科学与工程（可...
- 6 篇 土木工程
- 6 篇 安全科学与工程
- 5 篇 化学工程与技术
- 5 篇 环境科学与工程（可...
- 4 篇 建筑学
- 4 篇 船舶与海洋工程
84 篇 理学
- 40 篇 数学
- 36 篇 生物学
- 28 篇 系统科学
- 20 篇 统计学（可授理学、...
- 15 篇 物理学
- 5 篇 化学
33 篇 管理学
- 28 篇 管理科学与工程(可...
- 12 篇 工商管理
10 篇 教育学
- 10 篇 教育学
9 篇 医学
3 篇 军事学
2 篇 经济学
2 篇 法学
1 篇 农学

主题

38 篇 reinforcement le...
20 篇 machine learning
18 篇 neural networks
15 篇 heuristic algori...
13 篇 adaptive control
12 篇 vehicle dynamics
12 篇 control systems
12 篇 dynamics
10 篇 optimization
10 篇 sliding mode con...
10 篇 trajectory
10 篇 deep reinforceme...
9 篇 optimal control
9 篇 robustness
8 篇 deep learning
8 篇 simulation
8 篇 robotics
7 篇 model predictive...
7 篇 nonlinear system...
7 篇 data models

机构

5 篇 mit cambridge ma...
4 篇 georgia inst tec...
4 篇 univ calif berke...
3 篇 univ calif san d...
3 篇 univ penn philad...
3 篇 stanford univ st...
3 篇 univ michigan de...
2 篇 ohio state univ ...
2 篇 duke univ durham...
2 篇 natl renewable e...
2 篇 school of electr...
2 篇 katholieke univ ...
2 篇 univ penn dept e...
2 篇 durban univ tech...
2 篇 delft univ techn...
2 篇 college of autom...
2 篇 univ bonn autono...
2 篇 zhejiang univ de...
2 篇 school of law he...
2 篇 carnegie mellon ...

作者

3 篇 vamvoudakis kyri...
3 篇 zavlanos michael...
3 篇 zhang baosen
3 篇 li na
3 篇 wang cong
3 篇 hazan elad
3 篇 cong wang
2 篇 gokdag mustafa
2 篇 soffker dirk
2 篇 michiels w.
2 篇 nakahira yorie
2 篇 cui wenqi
2 篇 zico kolter j.
2 篇 tomizuka masayos...
2 篇 fradkov alexande...
2 篇 pravesh
2 篇 levine sergey
2 篇 minasyan edgar
2 篇 pavlichenko dmyt...
2 篇 fan chuchu

语言

466 篇 英文
8 篇 其他
5 篇 中文

检索条件"任意字段=5th Annual Conference on Learning for Dynamics and Control"

共 478 条记录，以下是11-20 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：

ARL-Based Multi-Action Market Making with Hawkes Processes and Variable Volatility 24

ARL-Based Multi-Action Market Making with Hawkes Processes a...

引用

5th International conference on AI in Finance

作者： Wang, Ziyi Ventre, Carmine Polukarov, Maria Kings Coll London Dept Informat London England Kings Coll London London England

ISBN: (纸本)9798400710810

We advance market-making strategies by integrating Adversarial Reinforcement learning (ARL), Hawkes Processes, and variable volatility levels while also expanding the action space available to market makers (MMs). To enhance the adaptability and robustness of these strategies - which can quote always, quote only on one side of the market or not quote at all - we shift from the commonly used Poisson process to the Hawkes process, which better captures real market dynamics and self-exciting behaviors. We then train and evaluate strategies under volatility levels of 2 and 200. Our findings show that the 4-action MM trained in a low-volatility environment effectively adapts to high-volatility conditions, maintaining stable performance and providing two-sided quotes at least 92% of the time. this indicates that incorporating flexible quoting mechanisms and realistic market simulations significantly enhances the effectiveness of market-making strategies.

关键词： High-Frequency Trading Market Making Limit Order Book Stochastic Optimal control Deep & Adversarial Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Policy learning for Active Target Tracking over Continuous SE(3) Trajectories 5

Policy Learning for Active Target Tracking over Continuous S...

引用

5th annual conference on learning for dynamics and control

作者： Yang, Pengzhi Koga, Shumon Asgharivaskasi, Arash Atanasov, Nikolay Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

this paper develops a model-based policy gradient algorithm for tracking dynamic targets using a mobile agent equipped with an onboard sensor with limited field of view. the task is to obtain a continuous control policy for the mobile agent to collect sensor measurements that reduce uncertainty in the target states, measured by the target distribution entropy. We design a neural network control policy with the agent SE(3) pose and the mean vector and information matrix of the joint target distribution as inputs and attention layers to handle variable numbers of targets. We also derive the gradient of the target entropy with respect to the network parameters explicitly, allowing efficient model-based policy gradient optimization.

关键词： Active target tracking model-based reinforcement learning SLAM

来源：评论

学校读者我要写书评

暂无评论

FedSysID: A Federated Approach to Sample-Efficient System Identification 5

FedSysID: A Federated Approach to Sample-Efficient System Id...

引用

5th annual conference on learning for dynamics and control

作者： Wang, Han Toso, Leonardo F. Anderson, James Columbia Univ New York NY 10027 USA

We study the problem of learning a linear system model from the observations of M clients. the catch: Each client is observing data from a different dynamical system. this work addresses the question of how multiple clients collaboratively learn dynamical models in the presence of heterogeneity. We pose this problem as a federated learning problem and characterize the tension between achievable performance and system heterogeneity. Furthermore, our federated sample complexity result provides a constant factor improvement over the single agent setting. Finally, we describe a meta federated learning algorithm, FedSysID, that leverages existing federated algorithms at the client level.

关键词： Federated learning System identification System heterogeneity

来源：评论

学校读者我要写书评

暂无评论

learning Coherent Clusters in Weakly-Connected Network Systems 5

Learning Coherent Clusters in Weakly-Connected Network Syste...

引用

5th annual conference on learning for dynamics and control

作者： Min, Hancheng Mallada, Enrique Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

We propose a structure-preserving model-reduction methodology for large-scale dynamic networks with tightly-connected components. First, the coherent groups are identified by a spectral clustering algorithm on the graph Laplacian matrix that models the network feedback. then, a reduced network is built, where each node represents the aggregate dynamics of each coherent group, and the reduced network captures the dynamic coupling between the groups. We provide an upper bound on the approximation error when the network graph is randomly generated from a weight stochastic block model. Finally, numerical experiments align with and validate our theoretical findings.

关键词： Spectral Clustering Network Systems Model Reduction

来源：评论

学校读者我要写书评

暂无评论

Hybrid Multi-agent Deep Reinforcement learning for Autonomous Mobility on Demand Systems 5

Hybrid Multi-agent Deep Reinforcement Learning for Autonomou...

引用

5th annual conference on learning for dynamics and control

作者： Enders, Tobias Harrison, James Pavone, Marco Schiffer, Maximilian Tech Univ Munich Munich Germany Google Res Brain Team Mountain View CA USA Stanford Univ Stanford CA USA

We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system. We formalize this problem as a Markov decision process and propose a novel combination of multi-agent Soft Actor-Critic and weighted bipartite matching to obtain an anticipative control policy. thereby, we factorize the operator's otherwise intractable action space, but still obtain a globally coordinated decision. Experiments based on real-world taxi data show that our method outperforms state of the art benchmarks with respect to performance, stability, and computational tractability.

关键词： hybrid learning and optimization multi-agent learning deep reinforcement learning autonomous mobility on demand

来源：评论

学校读者我要写书评

暂无评论

learning the dynamics of autonomous nonlinear delay systems 5

Learning the dynamics of autonomous nonlinear delay systems

引用

5th annual conference on learning for dynamics and control

作者： Ji, Xunbi A. Orosz, Gobor Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Univ Michigan Dept Civil Environm Engn Ann Arbor MI USA

In this paper, we focus on learning the time delay and nonlinearity of autonomous dynamical systems using trainable time delay neural networks. We demonstrate that, with delays trained together with weights and biases, the trained neural networks may approximate the right hand side of delay differential equations. It is shown that data collected from the vicinity a stable equilibrium or limit cycle do not contain rich enough dynamics, therefore the trained networks can have very poor generalization. However, including data about the transient behavior can significantly enhance the performance, and similar improvements can be achieved when data collected near a chaotic attractor is utilized. We also evaluate how the learning performance is affected by the selected loss function and measurement noise. Numerical results are presented for learning examples: Mackey-Glass equation and a predator-prey model.

关键词： trainable time delay neural networks autonomous nonlinear systems

来源：评论

学校读者我要写书评

暂无评论

Best of Both Worlds in Online control: Competitive Ratio and Policy Regret 5

Best of Both Worlds in Online Control: Competitive Ratio and...

引用

5th annual conference on learning for dynamics and control

作者： Goel, Gautam Agarwal, Naman Singh, Karan Hazan, Elad Univ Calif Berkeley Simons Inst Berkeley CA 94720 USA Google AI Princeton Princeton NJ USA Carnegie Mellon Univ Tepper Sch Business Pittsburgh PA USA Princeton Univ Dept Comp Sci Princeton NJ USA

We consider the fundamental problem of online control of a linear dynamical system from two different viewpoints: regret minimization and competitive analysis. We prove that the optimal competitive policy is well-approximated by a convex parameterized policy class, known as a disturbance-action control (DAC) policies. Using this structural result, we show that several recently proposed online control algorithms achieve the best of both worlds: sublinear regret vs. the best DAC policy selected in hindsight, and optimal competitive ratio, up to an additive correction which grows sublinearly in the time horizon. We further conclude that sublinear regret vs. the optimal competitive policy is attainable when the linear dynamical system is unknown, and even when a stabilizing controller for the dynamics is not available a priori.

关键词： Nonstochastic control regret minimization competitive ratio

来源：评论

学校读者我要写书评

暂无评论

learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided Exploration 6

Learning to Stabilize High-dimensional Unknown Systems Using...

引用

6th annual learning for dynamics and control conference

作者： Zhang, Songyuan Fan, Chuchu MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

Designing stabilizing controllers is a fundamental challenge in autonomous systems, particularly for high-dimensional, nonlinear systems that can hardly be accurately modeled with differential equations. the Lyapunov theory offers a solution for stabilizing control systems, still, current methods relying on Lyapunov functions require access to complete dynamics or samples of system executions throughout the entire state space. Consequently, they are impractical for high-dimensional systems. this paper introduces a novel framework, LYapunov-Guided Exploration (LYGE), for learning stabilizing controllers tailored to high-dimensional, unknown systems. LYGE employs Lyapunov theory to iteratively guide the search for samples during exploration while simultaneously learning the local system dynamics, control policy, and Lyapunov functions. We demonstrate its scalability on highly complex systems, including a high-fidelity F-16 jet model featuring a 16D state space and a 4D input space. Experiments indicate that, compared to prior works in reinforcement learning, imitation learning, and neural certificates, LYGE reduces the distance to the goal by 50% while requiring only 5% to 32% of the samples. Furthermore, we demonstrate that our algorithm can be extended to learn controllers guided by other certificate functions for unknown systems.

关键词： Lyapunov-guided Exploration High-dimensional Unknown Systems Machine learning

来源：评论

学校读者我要写书评

暂无评论

Physics-enhanced Gaussian Process Variational Autoencoder 5

Physics-enhanced Gaussian Process Variational Autoencoder

引用

5th annual conference on learning for dynamics and control

作者： Beckers, thomas Wu, Qirui Pappas, George J. Vanderbilt Univ Dept Comp Sci Nashville TN 37235 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA USA

Variational autoencoders allow to learn a lower-dimensional latent space based on high-dimensional input/output data. Using video clips as input data, the encoder may be used to describe the movement of an object in the video without ground truth data (unsupervised learning). Even though the object's dynamics is typically based on first principles, this prior knowledge is mostly ignored in the existing literature. thus, we propose a physics-enhanced variational autoencoder that places a physical-enhanced Gaussian process prior on the latent dynamics to improve the efficiency of the variational autoencoder and to allow physically correct predictions. the physical prior knowledge expressed as linear dynamical system is here reflected by the Green's function and included in the kernel function of the Gaussian process. the benefits of the proposed approach are highlighted in a simulation with an oscillating particle.

关键词： physics-enhance learning scientific machine learning variational autoencoders Gaussian processes

来源：评论

学校读者我要写书评

暂无评论

Hyperparameter Tuning of an Off-Policy Reinforcement learning Algorithm for H∞ Tracking control 5

Hyperparameter Tuning of an Off-Policy Reinforcement Learnin...

引用

5th annual conference on learning for dynamics and control

作者： Farahmandi, Alireza Reitz, Brian Debord, Mark Philbrick, Douglas Estabridis, Katia Hewer, Gary Naval Air Warfare Ctr Weap Div China Lake CA 93555 USA

In this work, we present the hyperparameter optimization of an online, off-policy reinforcement learning algorithm based on a parallel search. Since this model-free learning algorithm solves the H-infinity optimal tracking problem iteratively using ordinary least squares regression, we propose using the condition number of the data matrix as a model-free measure for tuning the hyperparameters. this addition enables automated optimization of the involved hyperparameters. We demonstrate that the condition number is a useful metric for tuning the number of collected samples, sampling interval, and other hyperparameters involved. In addition, we demonstrate a correlation between this condition number and properties of the sum of sinusoids persistent excitation.

关键词： Reinforcement learning Hyperparameter tuning Condition number Optimization Infinite horizon optimal control

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48页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：