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

Survival Tree01:19

Survival Tree

160
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
160
Multimachine Stability01:25

Multimachine Stability

230
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
230
Typical Model Studies01:30

Typical Model Studies

441
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
441
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

142
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
142
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

258
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
258
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

590
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
590

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

Model reduction of dynamical systems with a novel data-driven approach: The RC-HAVOK algorithm.

Chaos (Woodbury, N.Y.)·2024
查看所有相关文章

相关实验视频

Updated: Sep 13, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.2K

储水库计算和多滚动吸引器:网络拓如何塑造预测性能.

G Yılmaz Bingöl1, E Günay1

  • 1Department of Electrical and Electronics Engineering, Erciyes University, Kayseri, Turkey.

Chaos (Woodbury, N.Y.)
|July 28, 2025
PubMed
概括

本研究探讨了用于建模复杂的多滚动吸引器的水库计算 (RC). 混合星网和网圈网络在重建混乱动态方面表现最好.

科学领域:

  • 非线性动力学是一种非线性动力学.
  • 计算神经科学是一种神经科学.
  • 复杂的系统复杂的系统.

背景情况:

  • 多滚动吸引器表现出高维的非线性动态,这带来了重大的建模挑战.
  • 储计算 (RC) 是一种用于混乱时间序列预测的强大工具,但它对多滚动吸引器的应用仍然未被探索.

研究的目的:

  • 为了研究储计算对模拟多滚动吸引器的有效性.
  • 系统地分析九种不同的网络拓对RC对这些系统的预测性能的影响.

主要方法:

  • 训练RC模型重建三个多滚动吸引器系统的相空间轨迹.
  • 使用最大利亚普诺夫指数 (LLE),根平均平方误差 (RMSE),平均平方误差 (MSE) 和平均绝对误差 (MAE) 评估性能.
  • 使用弗罗贝尼乌斯规范分析网络结构性质,以将连接性与准确性相关联.

主要成果:

  • 星网和网圈混合网络在多滚动吸引器重建中表现出卓越的性能,具有最低的错误率.
  • 随机和网格网络显示出更高的错误率,表明预测能力有限.
  • 弗罗贝尼乌斯规范分析显示,适度的网络连接优化了吸引器重建的准确性.

结论:

更多相关视频

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.3K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K

相关实验视频

Last Updated: Sep 13, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.2K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.3K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K
  • 混合网络拓 (星状网,网状环) 对于使用水库计算的多滚动吸引器建模非常有效.
  • 网络连接对于混乱系统的RC模型的预测准确性起着至关重要的作用.
  • 结果为优化复杂非线性动态的RC架构提供了洞察力.