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

Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

647
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
647
Random Variables01:09

Random Variables

12.2K
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...
12.2K
Multiple Regression01:25

Multiple Regression

3.0K
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...
3.0K
Distributed Loads01:19

Distributed Loads

539
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
539
Regression Analysis01:11

Regression Analysis

5.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.7K

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

Comparative Analysis of Gut Microbiome Diversity, Stability, and Predicted Function in Captive Guanacos (<i>Lama guanicoe</i>) and Alpacas (<i>Vicugna pacos</i>).

Microorganisms·2026
Same author

Virus-like particles in cancer immunotherapy: bridging human and veterinary medicine through one health.

Journal of nanobiotechnology·2026
Same author

A 20-year bibliometric analysis on the development trends and hotspots of pectus excavatum research: 2005-2025.

Journal of thoracic disease·2026
Same author

Multi-omics profiling identifies HMGA2 fusions as defining a distinct and prognostically favorable subtype of dedifferentiated liposarcoma with rhabdomyosarcomatous differentiation.

Human pathology·2026
Same author

Multi-target positioning and motion tracking enabled by a compound meta-eye system.

Microsystems & nanoengineering·2026
Same author

Switchable thermoresponsive fluorinated hydrogels for reversible and efficient PFAS adsorption-desorption.

Water research·2026
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: Jul 9, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

767

基于长期短期记忆的双支持向量的回归,用于概率负载预测.

Zichen Zhang, Yongquan Dong, Wei-Chiang Hong

    IEEE transactions on neural networks and learning systems
    |November 29, 2023
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的概率负载预测模型,BFEEMD-LSTM-TWSVRSOA,提高了电力系统的效率. 与现有方法相比,该模型显著提高了点和概率负载预测的准确性.

    更多相关视频

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.1K
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.3K

    相关实验视频

    Last Updated: Jul 9, 2025

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    767
    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.1K
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.3K

    科学领域:

    • 电力系统工程 电力系统工程
    • 计算智能是一种计算智能.
    • 数据科学数据科学数据科学

    背景情况:

    • 准确可靠的概率负载预测对于高效的电力系统运行和能源资源管理至关重要.
    • 预测模型和非静止电荷数据中的不确定性估计是一个重大挑战.

    研究的目的:

    • 提出一种新的概率负载预测模型,BFEEMD-LSTM-TWSVRSOA,旨在估计预测模型和非静止电荷数据中的不确定性.
    • 用2014年全球能源预测竞赛数据集对各种机器学习和深度学习算法进行对拟议模型的性能评估.

    主要方法:

    • 拟议的模型整合了用于数据过的快速组合实证模式分解 (FEEMD),用于特征提取的长短期内存 (LSTM) 网络和用于预测的双支持向量回归 (TWSVR).
    • 参数使用搜索器优化算法 (SOA) 进行优化.
    • 分析了引导方法和预测步骤大小,以确定最佳预测间隔和点预测结果.

    主要成果:

    • 来自GEFCom2014的四个季节性数据集的实验结果表明,野生引导方法和24小时的步骤大小是拟议模型的最佳选择.
    • BFEEMD-LSTM-TWSVRSOA模型取得了显著的改进,在数据集中的点预测中平均超过了低于最佳模型的46%和概率预测的53%.

    结论:

    • 与现有方法相比,拟议的BFEEMD-LSTM-TWSVRSOA模型在点和概率负载预测方面表现出优异的性能.
    • 该研究验证了FEEMD,LSTM,TWSVR和SOA组件在提高负载预测准确性和可靠性的有效性.