Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

State Space Representation01:27

State Space Representation

534
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
534
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

347
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
347
Transfer Function to State Space01:23

Transfer Function to State Space

765
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
765
State Space to Transfer Function01:21

State Space to Transfer Function

560
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
560
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

394
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
394
Modeling with Differential Equations01:25

Modeling with Differential Equations

20
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
20

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Quantifying the Waddington landscape and biological paths for development and differentiation.

Proceedings of the National Academy of Sciences of the United States of America·2011
Same author

[Evaluation on the effects of an education program regarding the sedentary behavior among school-aged children using Transtheoretical Model].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2011
Same author

[The application of near-infrared diffuse reflection spectra based on the principle of linear additive in tobacco redrying formula].

Guang pu xue yu guang pu fen xi = Guang pu·2011
Same author

Genetic study of families affected with aggressive periodontitis.

Periodontology 2000·2011
Same author

[Experimental study on Qi deficiency and blood stasis induced by muti-factor stimulation in rats].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2011
Same author

Improved visible light photocatalytic activity of sphere-like BiOBr hollow and porous structures synthesized via a reactable ionic liquid.

Dalton transactions (Cambridge, England : 2003)·2011
Same journal

Anchor-based disentanglement framework for incremental multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Complex-valued amplitude-phase interference modeling for adversarially robust classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jan 17, 2026

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
09:46

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

13.1K

多视图学习与状态空间模型相遇:一个动态系统视角

Weibin Chen1, Ying Zou1, Zhiyong Xu1

  • 1College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou University, Fuzhou, 350108, China.

Neural networks : the official journal of the International Neural Network Society
|September 18, 2025
PubMed
概括
此摘要是机器生成的。

多视图状态空间模型 (MvSSM) 为多视图表示学习提供了一种新的动态系统方法. 这种可解释的框架增强了功能集成和预测,优于现有的方法.

关键词:
动态系统是一个动态系统.图表神经网络的神经网络模型的解释性 模型的解释性多视图学习多视图学习半监督的分类是半监督的分类国家空间模型.

更多相关视频

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K
Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

11.1K

相关实验视频

Last Updated: Jan 17, 2026

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
09:46

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

13.1K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K
Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

11.1K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 控制理论 控制理论

背景情况:

  • 多视图学习利用多种数据模式来提高任务性能.
  • 现有的深度学习方法往往缺乏动态特征表示的原则框架,阻碍了可解释性.
  • 需要统一的,可解释的模型来捕捉多视图特征的演变.

研究的目的:

  • 引入多视图状态空间模型 (MvSSM) 进行原则性的多视图表示学习.
  • 开发一个框架,将功能集成和标签预测集成到一个单一的可解释模型中.
  • 为了使系统动态和表示转换在多视图学习中的理论分析.

主要方法:

  • 制定多视图表示学习作为一个由控制理论启发的连续时间动态系统.
  • 将视图特定特征视为外部输入,并将共享的潜在表示视为不断演变的系统状态.
  • 开发使用拉普拉斯和逆拉普拉斯转换的MvSSM变体 (MvSSM-Lap,MvSSM-iLap).用于系统动态.

主要成果:

  • 该MvSSM框架统一功能集成和标签预测,允许进行理论分析.
  • MvSSM变体与图形卷积具有结构上的相似性,从而促进了高效的特征传播.
  • 对IAPR-TC12和ESP数据集的实验结果表明,与最先进的方法相比,性能有了显著的提高.

结论:

  • 该MvSSM提供了一个理论上有基础的和可解释的方法,多视图表示学习.
  • 拟议的动态系统框架增强了特征演变和交叉视图交互的建模.
  • MvSSM实现了卓越的性能,突出了其在推进多视角学习研究方面的潜力.