Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

403
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
403
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.6K
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...
9.6K
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

112
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
112
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

398
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
398
Linearization and Approximation01:26

Linearization and Approximation

92
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
92
Trimmed Mean01:10

Trimmed Mean

3.5K
While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
3.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

HER2 assessment in locally advanced gastric cancer: comparing the results obtained with the use of two primary tumour blocks versus those obtained with the use of all primary tumour blocks.

Histopathology·2017
Same author

Inflammatory microRNA-194 and -515 attenuate the biosynthesis of chondroitin sulfate during human intervertebral disc degeneration.

Oncotarget·2017
Same author

Soil Acidification Aggravates the Occurrence of Bacterial Wilt in South China.

Frontiers in microbiology·2017
Same author

Is the Prophylactic Use of Hepatoprotectants Necessary in Anti-Tuberculosis Treatment?

Chemotherapy·2017
Same author

Light-induced aggregation of microbial exopolymeric substances.

Chemosphere·2017
Same author

Chemical Synthesis of (+)-Ryanodine and (+)-20-Deoxyspiganthine.

ACS central science·2017

Related Experiment Video

Updated: Feb 22, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.2K

A Novel Pruning Algorithm for Smoothing Feedforward Neural Networks Based on Group Lasso Method.

Jian Wang, Chen Xu, Xifeng Yang

    IEEE Transactions on Neural Networks and Learning Systems
    |September 30, 2017
    PubMed
    Summary

    This study introduces novel backpropagation algorithm variants inspired by Group Lasso to enhance feedforward neural network generalization. These methods effectively address Group Lasso

    Related Experiment Videos

    Last Updated: Feb 22, 2026

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
    06:45

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

    Published on: October 28, 2022

    2.2K

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Neural Networks

    Background:

    • Feedforward neural networks require methods to improve generalization ability.
    • Group Lasso offers variable selection at the group level but faces numerical challenges.
    • Existing penalization methods like Weight Decay have limitations.

    Purpose of the Study:

    • To propose four new variants of the backpropagation algorithm.
    • To improve the generalization ability of feedforward neural networks.
    • To overcome the drawbacks of direct Group Lasso penalty application.

    Main Methods:

    • Developed four backpropagation algorithm variants based on Group Lasso.
    • Introduced smoothing functions to approximate the Group Lasso penalty.
    • Employed numerical experiments for classification and regression tasks.

    Main Results:

    • Proposed algorithms demonstrated superior performance over Weight Decay, Weight Elimination, and Approximate Smoother.
    • Achieved better generalization and pruning efficiency in numerical experiments.
    • Verified advantages through detailed simulations on the MNIST dataset.

    Conclusions:

    • The novel backpropagation variants effectively improve neural network generalization.
    • Smoothed Group Lasso approximations overcome numerical and theoretical challenges.
    • The proposed strategy offers significant advantages in network pruning and efficiency.