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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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

End Point Prediction: Gran Plot

190
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...
190
Econometric Views (EViews)01:29

Econometric Views (EViews)

90
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
90
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
Random Variables01:09

Random Variables

11.3K
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...
11.3K

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

Gab1 but not Grb2 mediates tumor progression in Met overexpressing colorectal cancer cells.

Carcinogenesis·2008
Same author

Long-term donor-specific tolerance in rat cardiac allografts by intrabone marrow injection of donor bone marrow cells.

Transplantation·2008
Same author

Lsr2 of Mycobacterium tuberculosis is a DNA-bridging protein.

Nucleic acids research·2008
Same author

Amphetamine selectively enhances avoidance responding to a less salient stimulus in rats.

Journal of neural transmission (Vienna, Austria : 1996)·2008
Same author

Retrospective analysis of anterior correction and fusion for adolescent idiopathic thoracolumbar/lumbar scoliosis: the relationship between preserving mobile segments and trunk balance.

International orthopaedics·2008
Same author

Intrarenal antigens activate CD4+ cells via co-stimulatory signals from dendritic cells.

Journal of the American Society of Nephrology : JASN·2008

相关实验视频

Updated: May 16, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K

基于激进激活函数的宽ESN用于预测具有多个变量的时间序列.

Yuanpeng Gong, Shuxian Lun, Ming Li

    IEEE transactions on neural networks and learning systems
    |May 14, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一个广的回声状态网络 (Broad-ESN),具有用于多维时间序列 (MTS) 预测的新型激素激活功能. 与现有方法相比,广播ESN模型显示出更高的预测准确性和更少的误差.

    更多相关视频

    O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
    06:50

    O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

    Published on: November 8, 2019

    6.5K
    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
    04:35

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

    Published on: July 3, 2020

    3.3K

    相关实验视频

    Last Updated: May 16, 2025

    A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
    10:46

    A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

    Published on: December 9, 2015

    10.6K
    O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
    06:50

    O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

    Published on: November 8, 2019

    6.5K
    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
    04:35

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

    Published on: July 3, 2020

    3.3K

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 时间序列分析时间序列分析

    背景情况:

    • 多维时间序列 (MTS) 由于其固有的多维性和多特征特征,提出了独特的挑战.
    • 选择合适的预测模型对于有效分析和预测MTS数据至关重要.
    • 现有的预测模型可能会在复杂的时间序列模式中遇到梯度消失和局部优化问题.

    研究的目的:

    • 为增强的多维时间序列 (MTS) 预测提出一个新的广回声状态网络 (Broad-ESN) 模型.
    • 引入激进激活功能,以减轻梯度消失,并改善复杂数据的处理.
    • 为了增强优化过程,以提高预测准确度.

    主要方法:

    • 开发了一个激进激活函数,以解决代过程中梯度消失的问题.
    • 使用滑窗技术从MTS中提取特征,由特征号确定储数量.
    • 皮亚德金鱼优化器 (PKO) 是使用立方混沌映射初始化,并通过指数螺旋方程进行优化,以防止局部优化.

    主要成果:

    • 拟议的广播ESN模型在预测性能方面明显优于现有模型.
    • 该模型实现了高预测准确性,并证明了低误差率.
    • 激进激活功能在处理复杂的数据模式方面被证明是有效的.

    结论:

    • 新的Broad-ESN模型在多维时间序列 (MTS) 预测方面取得了重大进展.
    • 激素激活功能的集成和优化的PKO增强了模型的稳定性和准确性.
    • 这种方法为MTS预测任务提供了更好的解决方案.