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

PI Controller: Design01:24

PI Controller: Design

507
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
507
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

208
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
208
Regression Toward the Mean01:52

Regression Toward the Mean

6.5K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.5K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.9K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.9K
Gradient and Del Operator01:14

Gradient and Del Operator

3.0K
In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
3.0K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

2.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
2.3K

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

Convergence analysis and application for high-order neural networks based on gradient descent learning algorithm via smooth regularization.

Scientific reports·2025
Same author

Exploring novel solitary wave phenomena in Klein-Gordon equation using <math></math> model expansion method.

Scientific reports·2025
Same author

A rapid liquid biopsy of lung cancer by separation and detection of exfoliated tumor cells from bronchoalveolar lavage fluid with a dual-layer "PERFECT" filter system.

Theranostics·2020
Same author

Depletion but Activation of CD56<sup>dim</sup>CD16<sup>+</sup> NK Cells in Acute Infection with Severe Fever with Thrombocytopenia Syndrome Virus.

Virologica Sinica·2020
Same author

Three-Dimensional Patterning of Nanoparticles by Molecular Stamping.

ACS nano·2020
Same author

Circulating tumor DNA predicts response in Chinese patients with relapsed or refractory classical hodgkin lymphoma treated with sintilimab.

EBioMedicine·2020

相关实验视频

Updated: Sep 16, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

基于批量梯度的平滑 L2/3 正规化用于训练pi-sigma高阶网络.

Khidir Shaib Mohamed1, Raed Muhammad Albadrani2, Ekram Adam3

  • 1Department of Mathematics, College of Sciences, Qassim University, Buraydah, Saudi Arabia. k.idris@qu.edu.sa.

Scientific reports
|July 8, 2025
PubMed
概括

本研究介绍了Pi-Sigma神经网络 (PSNNs) 的平滑L2/3规范化方法,以提高模型稀疏性和学习速度. 新方法克服了振荡问题,并在模拟中展示了卓越的性能.

关键词:
批量梯度方法的批量梯度方法.数字结果的数值结果.皮-西格玛神经网络的神经网络光滑 L 2/3 调整规则化

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

1.3K

相关实验视频

Last Updated: Sep 16, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K
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

1.3K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 神经网络的神经网络的神经网络

背景情况:

  • 皮-西格玛神经网络 (PSNNs) 将用于函数近似的前网络泛化.
  • L2/3 调整促进了稀疏的建模,但由于不平滑,可能会导致振荡.

研究的目的:

  • 为PSNNs开发一个平滑的L2/3规范化方法.
  • 增强模型稀疏性,加速PSNN中的学习.
  • 为了解决与传统L2/3调节相关的振荡问题.

主要方法:

  • 为PSNN提出了一种新的光滑L2/3调节器.
  • 用新的调节器对PSNN的弱和强收特性进行分析.
  • 将学习率和惩罚参数联系起来,以确保趋同.

主要成果:

  • 平滑L2/3调节器有效消除振荡现象.
  • 与最初的L2/3规范化相比,表现出显著的性能改善.
  • 模拟结果支持理论上的收结果.

结论:

  • 拟议的平滑L2/3规范化对PSNN有效.
  • 这种方法增强了稀疏性,加快了学习速度,并确保了趋同.
  • 这种方法为PSNN提供了比传统的L2/3规范化更强大的替代方案.