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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

90
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...
90
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

234
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...
234
Random Variables01:09

Random Variables

11.4K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
11.4K
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
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...
93

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

The relationship between digital health literacy, self-efficacy, and self-management behaviors in patients with diabetes-related foot disease: a cross-sectional study.

Frontiers in public health·2026
Same author

Heterogeneous oblique double random forest.

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

Targeted Isolation of Novel Calcium-Binding Peptides via Hydroxyapatite Affinity and Their Promotion Effect on Osteogenesis in MC3T3-E1 Cells.

Journal of food science·2026
Same author

Surgical strategies for spontaneous intracerebral hemorrhage: a Bayesian network meta-analysis of randomized controlled trials.

Frontiers in neurology·2026
Same author

Long-Range Order in a Strictly Short-Range Quasi-2D XY Model: When Critical Fluctuations Matter.

Physical review letters·2026
Same author

Entagenic acid targets ASCC2 to ameliorate allergic contact dermatitis via repressing NF‑κB transactivation and chemokine expression.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026

相关实验视频

Updated: May 24, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

475

堆叠组合深度随机向量功能链接网络与残余学习用于中等规模的时间序列预测.

Ruobin Gao, Minghui Hu, Ruilin Li

    IEEE transactions on neural networks and learning systems
    |March 3, 2025
    PubMed
    概括

    一个新的模型,SResdRVFL,通过整合剩余学习和堆叠的深层块来增强集体深度随机向量功能链接 (dRVFL) 网络. 这种方法提高了多样性和错误纠正,在28个数据集上表现优于现有的方法.

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 随机神经网络,包括深度随机向量功能链 (dRVFL) 和集体dRVFL (edRVFL),表现强.
    • 现有的edRVFL架构在网络中缺乏多样性和独立的错误纠正能力.

    研究的目的:

    • 引入全新组合深度随机向量功能链接网络架构,增强多样性和错误纠正.
    • 开发基于残留学习的dRVFL (ResdRVFL) 和集体深层堆叠网络 (SResdRVFL),改进现有方法.

    主要方法:

    • 将堆叠的深层块和残留学习与edRVFL框架结合起来.
    • 建议ResdRVFL,其中深层纠正浅层估计,并包含一个缩放参数来控制剩余缩放并防止过.
    • 通过聚合多个ResdRVFL块来开发SResdRVFL,以便组合学习.

    主要成果:

    • 拟议的SResdRVFL模型在28个不同的数据集上进行了评估.
    • 对比分析显示,SResdRVFL在平均排名和错误率方面表现优于最先进的方法.

    结论:

    • 该SResdRVFL架构有效地解决了现有的edRVFL模型中的局限性.

    更多相关视频

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    938
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    451

    相关实验视频

    Last Updated: May 24, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    475
    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    938
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    451
  • 剩余学习和集体深层堆叠的整合在各种机器学习任务中提供了卓越的性能和稳定性.