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

27 篇 会议
15 篇 期刊文献

馆藏范围

42 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

20 篇 工学
- 18 篇 控制科学与工程
- 10 篇 电气工程
- 10 篇 电子科学与技术（可...
- 7 篇 信息与通信工程
- 7 篇 计算机科学与技术...
- 6 篇 软件工程
18 篇 理学
- 17 篇 数学
- 13 篇 统计学（可授理学、...
- 12 篇 系统科学
3 篇 管理学
- 2 篇 管理科学与工程(可...
- 1 篇 图书情报与档案管...

主题

20 篇 state estimation
14 篇 stochastic syste...
5 篇 kalman filters
5 篇 estimation theor...
5 篇 covariance matri...
4 篇 kalman filtering
4 篇 approximation me...
4 篇 nonlinear system...
4 篇 nonlinear filter...
3 篇 monte carlo meth...
3 篇 nonlinear filter...
2 篇 sample size
2 篇 filtering
2 篇 target tracking
2 篇 software framewo...
2 篇 particle measure...
2 篇 noise
2 篇 dual control
2 篇 nonlinear state ...
2 篇 monte carlo meth...

机构

4 篇 department of cy...
4 篇 department of cy...
4 篇 research centre ...
3 篇 department of cy...
3 篇 research centre ...
2 篇 department of cy...
2 篇 department of cy...
2 篇 department of cy...
1 篇 cybernetics rese...
1 篇 department of cy...
1 篇 department of cy...
1 篇 research centre ...
1 篇 department of cy...
1 篇 department of cy...
1 篇 research centre ...
1 篇 department of cy...
1 篇 department of cy...
1 篇 research centre ...
1 篇 department of cy...
1 篇 department of cy...

作者

18 篇 šimandl miroslav
14 篇 miroslav šimandl
13 篇 ondřej straka
10 篇 jindřich duník
9 篇 straka ondřej
7 篇 flídr miroslav
7 篇 duník jindřich
5 篇 miroslav flídr
5 篇 miroslav ŝimandl
3 篇 straka ondšej
3 篇 ondrej straka
2 篇 duník jindšich
2 篇 jindrich duník
2 篇 jaroslav ŝvácha
2 篇 miroslav simandl
2 篇 jindrich dunik
2 篇 švácha jaroslav
1 篇 jiří ajgl
1 篇 simandl miroslav
1 篇 miroslav s̆imand...

语言

42 篇 英文

检索条件"机构=Department of Cybernetics and Research Centre Data-Algorithms-Decision Making"

共 42 条记录，以下是1-10 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 choice of scaling parameter in derivative-free local filters

Adaptive choice of scaling parameter in derivative-free loca...

引用

作者： Duník, Jindřich Šimandl, Miroslav Straka, Ondřej Department of Cybernetics Research Centre Data-Algorithms-Decision Making University of West Bohemia Czech Republic

ISBN: (纸本)9780982443811

The paper deals with adaptive choice of the scaling parameter in derivative-free local filters. In the last decade several novel local derivative-free filtering methods have been proposed. These methods exploiting Stirling's interpolation and the unscented transformation are, however, conditioned by specification of a scaling parameter significantly influencing the quality of the state estimate. Surprisingly, almost no attention has been devoted to a suitable choice of the parameter. In fact, only a few basic recommendations have been provided, which are rather general and do not respect the particular system description. The choice of the parameter thus remains mainly on a user. The goal of the paper is to provide a technique for adaptive choice of the scaling parameter of the derivative-free local filters.

关键词： Nonlinear filtering

来源：评论

学校读者我要写书评

暂无评论

Gaussian mixtures proposal density in particle filter for track-before-detect

Gaussian mixtures proposal density in particle filter for tr...

引用

2009 12th International Conference on Information Fusion, FUSION 2009

作者： Straka, Ondrej Šimandl, Miroslav Duník, Jindrich Department of Cybernetics Research Centre Data-Algorithms-Decision Making University of West Bohemia Czech Republic

ISBN: (纸本)9780982443804

The paper deals with state estimation for the track-before-detect approach using the particle filter. The focus is aimed at the track initiation proposal density of the particle filter which considerably affects estimate qual-ity. The goal of the paper is to design a proposal based on a Gaussian mixture using a bank of extended Kalman filters. This leads to root mean square error lower than that achieved by usual simple track initiation proposals. Due to application of several developed techniques reducing computational requirements of the designed proposal, the Gaussian mixture particle filter also achieves lower com-putational requirements than ordinary particle filter. Per-formance of the proposed Gaussian mixture track initiation proposal in the particle filter is demonstrated in a numerical example. ©2009 ISIF.

关键词： Nonlinear filtering

来源：评论

学校读者我要写书评

暂无评论

Truncated unscented particle filter

Truncated unscented particle filter

引用

作者： Straka, Ondřej Duník, Jindřich Šimandl, Miroslav Research Centre Data-Algorithms-Decision Making Department of Cybernetics University of West Bohemia Pilsen Czech Republic

ISBN: (纸本)9781457700804

The problem of state estimation of nonlinear stochastic dynamic systems with nonlinear inequality constraints is treated. The paper focuses on a particle filtering approach, which provides an estimate of the state in the form of a probability density function. A new computationally efficient particle filter for the constrained estimation problem is proposed. The importance function of the particle filter is generated by the unscented Kalman filter that is supplemented with a designed truncation technique to accommodate the constraint. The proposed filter is illustrated in a numerical example. © 2011 AACC American Automatic Control Council.

关键词： State estimation

来源：评论

学校读者我要写书评

暂无评论

A software framework and tool for nonlinear state estimation

A software framework and tool for nonlinear state estimation

引用

15th IFAC Symposium on System Identification, SYSID 2009

作者： Straka, Ondšej Flídr, Miroslav Duník, Jindšich Šimandl, Miroslav Department of Cybernetics and Research Centre Data - Algorithms - Decision Making University of West Bohemia Univerzitní 8 30614 Plzeň Czech Republic

ISBN: (纸本)9783902661470

The goal of the article is to describe a software framework designed for nonlinear state estimation of discrete time dynamic systems. The framework was designed with the aim to facilitate implementation, testing and use of various nonlinear state estimation methods in mind. The main strength of the framework is its versatility due to the possibility of either structural or probabilistic description of the problem. Besides the well-known basic nonlinear estimation methods such as the extended Kalman filter, the divided difference filters and the unscented Kalman filter, the framework implements particle filter with advanced features as well. As the framework is designed on the object oriented basis, further extension by user-specified nonlinear estimation algorithms is extremely easy. The paper provides a brief introduction into nonlinear state estimation problem and describes the individual components of the framework, their key features and use. The strengths of the framework are presented in two examples. © 2009 IFAC.

关键词： Nonlinear analysis

来源：评论

学校读者我要写书评

暂无评论

Framework for implementing and testing nonlinear filters

Framework for implementing and testing nonlinear filters

引用

作者： Flídr, Miroslav Duník, Jindřich Straka, Ondřej Švácha, Jaroslav Šimandl, Miroslav Department of Cybernetics and Research Centre Data - Algorithms - Decision Making University of West Bohemia In Pilsen Univerzitní 8 30614 Plzeň Czech Republic

ISBN: (纸本)9783902661074

The aim of this paper is to present a software framework facilitating implementation, testing and use of various nonlinear estimation methods. This framework is designed to offer an easy to use tool for state estimation of discrete time dynamic stochastic systems. Besides implementation of various local and global state estimation methods it contains procedures for system design and simulation. Its strength is in the fact that it provides means that help students get acquainted with nonlinear state estimation problem and to be able to test features of various estimation methods. Another considerable advantage of proposed framework is its high modularity and extensibility. The paper briefly describes nonlinear estimation problem and its general solution using the Bayesian approach leading to the Bayesian recursive relations. Then it presents key features of the software framework designed in MATLAB environment that supports straightforward implementation of estimation methods based on the Bayesian approach. The strengths of the framework are demonstrated on implementation of the Divided difference filter 1st order. Copyright © 2006 IFAC.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Nonlinear filtering toolbox for continuous stochastic systems with discrete measurements

Nonlinear filtering toolbox for continuous stochastic system...

引用

作者： Švácha, Jaroslav Šimandl, Miroslav Straka, Ondřej Flídr, Miroslav Research Centre Data Algorithms and Decision Making Department of Cybernetics University of West Bohemia In Pilsen Univerzitní 8 306 14 Plzeň Czech Republic

ISBN: (纸本)9783902661074

The paper deals with a problem of state estimation for nonlinear continuous stochastic systems with discrete-time measurements. A general recursive solution of the estimation problem given by the Bayesian rule and by the Fokker-Planck equation is described. Local estimation methods employing analytical approach to solution and global estimation methods employing analytical, numerical and simulation approaches are discussed. A software package for state estimation of continuous stochastic systems with discrete-time measurements is developed and described. It serves for system design, system simulation, estimator setup and state estimation. The package is designed to embody easily user defined estimators and thus it is suitable for estimator testing and quality comparison of different estimators. Usage of the nonlinear filtering software package is illustrated in a numerical example. Copyright © 2006 IFAC.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Particle based probability density fusion with differential Shannon entropy criterion

Particle based probability density fusion with differential ...

引用

作者： Ajgl, Jiří Šimandl, Miroslav Department of Cybernetics and Research Centre Data - Algorithms - Decision Making Faculty of Applied Sciences University of West Bohemia Pilsen Czech Republic

ISBN: (纸本)9781457702679

This paper focuses on a decentralised nonlinear estimation problem in a multiple sensor network. The stress is laid on the optimal fusion of probability densities conditioned by different data. The probability density conditioned by the common data is supposed to be unavailable. The optimal fusion is elaborated in the particle filtering and differential Shannon entropy framework. The conversion of weighted particles into a continuous probability density function is performed implicitly by the time update. Further, the issue of sampling density proposal is explored. The proposed approach is illustrated in numerical examples. © 2011 IEEE.

关键词： Monte Carlo methods

来源：评论

学校读者我要写书评

暂无评论

An efficient constrained Gaussian particle filter

An efficient constrained Gaussian particle filter

引用

作者： Straka, Ondřej Šimandl, Miroslav Department of Cybernetics and Research Centre Data-Algorithms-Decision Making Faculty of Applied Sciences University of West Bohemia Univerzitní 8 306 14 Pilsen Czech Republic

ISBN: (纸本)9783902661937

The paper deals with a state estimation of nonlinear stochastic dynamic systems subject to a nonlinear inequality constraint. A special focus is paid to particle filters, which provide an estimate of the whole probability density as opposed to the local filters, such as the extended Kalman filter or the unscented Kalman filter, which provide a point estimate only. Within the particle filtering framework, there are several approaches to the constrained state estimation, mostly based on discarding samples violating the constraint with a possible increase of their number to improve the estimate quality. The paper aims at proposing a modification to an importance function of the particle filter in order to increase efficiency of sampling while keeping the computational complexity low. The proposed modification is utilized within the Gaussian particle filter which is advantageous for its low computational complexity. Complexity and estimation quality of the proposed constrained Gaussian particle filter is compared to other constrained particle filters in a numerical example. © 2011 IFAC.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

A survey of sample size adaptation techniques for particle filters

A survey of sample size adaptation techniques for particle f...

引用

15th IFAC Symposium on System Identification, SYSID 2009

作者： Straka, Ondšej Šimandl, Miroslav Department of Cybernetics and Research Centre: Data - Algorithms - Decision Making Faculty of Applied Sciences University of West Bohemia Univerzitní 8 306 14 Plzeň Czech Republic

ISBN: (纸本)9783902661470

The paper deals with the particle filter in discrete-time nonlinear non-Gaussian system state estimation. One of the key parameters affecting estimate quality of the particle filter is the sample size. In the literature, there is a number of techniques coming from various ideas that aim at adapting the sample size while keeping quality in some sense fixed. The goal of the paper is to provide a survey of sample size adaptation techniques, to classify them and to discuss various aspects concerning the techniques. © 2009 IFAC.

关键词： State estimation

来源：评论

学校读者我要写书评

暂无评论

The development of a randomised unscented Kalman filter

The development of a randomised unscented Kalman filter

引用

作者： Duník, Jindřich Straka, Ondřej Šimandl, Miroslav Department of Cybernetics and Research Centre Data-Algorithms-Decision Making Faculty of Applied Sciences University of West Bohemia Univerzitní 8 306 14 Pilsen Czech Republic

ISBN: (纸本)9783902661937

The paper deals with state estimation of nonlinear stochastic dynamic systems. Traditional filters providing local estimates of the states, such as the extended Kalman filter, unscented Kalman filter or the cubature Kalman filter, are based on approximations which lead to biased estimates of the state and measurement statistics. The aim of the paper is to propose a new local filter that utilises a randomised unscented transformation which is a special case of stochastic integration rules providing an unbiased estimate of an integral. The new filter provides estimates of higher quality than the traditional filters and renders a randomised version of the unscented Kalman filter. The proposed filter is illustrated in a numerical example. © 2011 IFAC.

关键词： State estimation

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5页 << < 1 2 3 4 5 > >>

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：