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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

88
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
88
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

134
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...
134
Neural Regulation01:37

Neural Regulation

39.6K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
39.6K
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

83
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
83

You might also read

Related Articles

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

Sort by
Same author

Clinical Spectrum, Heteroplasmy-Phenotype Correlation, and Prognosis of the MT-ND3 m.10191 T > C Mutation.

CNS neuroscience & therapeutics·2026
Same author

Matrix Commitment-based Ownership Verification for Distributed Machine Learning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

RNA Sequencing Resolves Cryptic Pathogenic Variants in Mitochondrial Disease.

Annals of clinical and translational neurology·2026
Same author

PromptSED: An evolving topic-enhanced prompting framework for incremental social event detection.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering.

Briefings in bioinformatics·2025
Same author

[Isongifolene Improves Crohn's Disease-Like Colitis in Mice by Reducing Apoptosis of Intestinal Epithelial Cells].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2024

Related Experiment Video

Updated: Aug 4, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Deep Fuzzy Min-Max Neural Network: Analysis and Design.

Wei Huang, Mingxi Sun, Liehuang Zhu

    IEEE Transactions on Neural Networks and Learning Systems
    |April 4, 2023
    PubMed
    Summary

    A new deep fuzzy min-max neural network (DFMNN) addresses sequential construction issues. This model uses simultaneous initialization and deep layer optimization to improve hyperbox design for better performance.

    More Related Videos

    Design and Analysis for Fall Detection System Simplification
    08:05

    Design and Analysis for Fall Detection System Simplification

    Published on: April 6, 2020

    10.8K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.3K

    Related Experiment Videos

    Last Updated: Aug 4, 2025

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    1.2K
    Design and Analysis for Fall Detection System Simplification
    08:05

    Design and Analysis for Fall Detection System Simplification

    Published on: April 6, 2020

    10.8K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.3K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Neural Networks

    Background:

    • Fuzzy min-max neural networks (FMNN) use sequential hyperbox construction.
    • This sequential method causes problems with input order and overlapping regions.

    Purpose of the Study:

    • To propose a deep fuzzy min-max neural network (DFMNN) model.
    • To overcome the limitations of sequential construction in traditional FMNNs.

    Main Methods:

    • Introduced an initialization operation for simultaneous hyperbox design, solving the input order problem.
    • Developed an optimization operation using deep layers to eliminate hyperbox overlap.
    • Each optimization layer includes partition, combination, and union sections to refine hyperboxes.

    Main Results:

    • The proposed DFMNN effectively addresses input order and hyperbox overlap issues.
    • Evaluated on benchmark datasets, DFMNN demonstrated superior performance.
    • Comparative analysis confirmed DFMNN outperforms existing models.

    Conclusions:

    • DFMNN offers a robust solution to FMNN limitations.
    • The deep architecture and novel operations enhance model efficiency and accuracy.
    • DFMNN represents a significant advancement in fuzzy neural network research.