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

Radial System Protection01:23

Radial System Protection

463
Radial systems employ time-delay overcurrent relays to reduce load interruptions. When a fault occurs, the nearest breaker opens first, while upstream breakers remain closed due to longer delay settings. This approach ensures minimal disruption to the rest of the system.
In a radial system with a fault downstream of the third breaker, ideally, only the third breaker will open, isolating the fault and interrupting the load connected beyond it. The second breaker has a longer delay setting,...
463
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

453
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 of...
453
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

673
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
673
Radius of Gyration of an Area01:12

Radius of Gyration of an Area

2.9K
The second moment of area, also known as the moment of inertia of area, is a crucial factor in understanding an object's resistance against bending deformation, or stiffness. To accurately estimate the second moment of area along any axis, one needs to concentrate all areas associated with that object into a thin strip, which should be placed parallel to that particular axis.
2.9K
Multiple Regression01:25

Multiple Regression

4.2K
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...
4.2K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

514
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...
514

You might also read

Related Articles

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

Sort by
Same author

Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI.

IEEE journal of translational engineering in health and medicine·2021
Same author

CTCF looping is established during gastrulation in medaka embryos.

Genome research·2021
Same author

Elastographic Tomosynthesis From X-Ray Strain Imaging of Breast Cancer.

IEEE journal of translational engineering in health and medicine·2019
Same author

A Novel Method of Cone Beam CT Projection Binning Based on Image Registration.

IEEE transactions on medical imaging·2017
Same author

Respiratory motion prediction and prospective correction for free-breathing arterial spin-labeled perfusion MRI of the kidneys.

Medical physics·2017
Same author

Intra- and Inter-Fractional Variation Prediction of Lung Tumors Using Fuzzy Deep Learning.

IEEE journal of translational engineering in health and medicine·2016
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
See all related articles

Related Experiment Video

Updated: Mar 8, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

852

Multicolumn RBF Network.

Ammar O Hoori, Yuichi Motai

    IEEE Transactions on Neural Networks and Learning Systems
    |January 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    The multicolumn RBF network (MCRN) enhances artificial neural network (ANN) performance by dividing data into subsets for parallel processing. This novel approach improves accuracy and significantly reduces computation time for large datasets.

    More Related Videos

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.4K

    Related Experiment Videos

    Last Updated: Mar 8, 2026

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    852
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.4K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Neural Networks

    Background:

    • Traditional Radial Basis Function Networks (RBFNs) face computational challenges with large datasets due to extensive kernel calculations.
    • The fully connected nature of RBFNs limits parallelization, increasing processing time for complex problems.

    Purpose of the Study:

    • To introduce the multicolumn RBF network (MCRN) as an efficient alternative to standard RBFNs.
    • To enhance both the accuracy and speed of RBF network computations, particularly for large-scale data.

    Main Methods:

    • The MCRN employs a k-d tree algorithm to partition datasets into smaller, manageable subsets.
    • Individual RBF networks are trained on these subsets and then integrated into a parallel MCRN structure for testing.

    Main Results:

    • The MCRN demonstrates superior accuracy and significantly improved training and testing times compared to single RBFNs.
    • Performance benchmarks show the MCRN outperforming other machine learning techniques like Support Vector Machines and k-Nearest Neighbors.

    Conclusions:

    • The MCRN offers a scalable and effective parallel architecture for improving RBF network efficiency.
    • Its regional specialization through data subsetting leads to better problem specification and overall performance gains.