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

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.1K
Introduction to Learning01:18

Introduction to Learning

533
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
533
Associative Learning01:27

Associative Learning

579
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
579
Machines: Problem Solving II01:30

Machines: Problem Solving II

373
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
373
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

411
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
411
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
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...
7.8K

You might also read

Related Articles

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

Sort by
Same author

Ubiquitination of DDX21 by HERC2 induces a dormancy-like phenotype via the NUCKS1-p21/p27 axis to promote radio-resistance in colorectal cancer cells.

Cell death & disease·2026
Same author

Machine Learning-Driven Prediction of Intensive Care Units Mortality and Length of Stay: A 11-Year Retrospective Study in Hong Kong Public Hospitals.

Journal of medical systems·2026
Same author

Gut Microbiota-Derived Tyrosol Alleviates Radiation-Induced Intestinal Injury via Targeting SCD1-MUFA Axis to Suppress ER Stress.

International journal of biological sciences·2026
Same author

A High-Accuracy Probabilistic-Based Sigmoid Approximator Incorporating Memory-Saving and Time-Efficient Strategies.

IEEE transactions on neural networks and learning systems·2026
Same author

High-dose corticosteroid application in acute severe nickel carbonyl poisoning: a case series of three occupational exposures and their recovery outcomes.

BMC pulmonary medicine·2026
Same author

Hypoxic tumor exosomes suppress macrophage inflammation and ferroptosis via NDUFV2 to enhance bystander tumor radioresistance.

Cell death & disease·2025
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Sep 13, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

584

Robust Fault-Aware Extreme Learning Machine Based on Maximum Correntropy.

Yuqi Xiao, Muideen Adegoke, Chi-Sing Leung

    IEEE Transactions on Neural Networks and Learning Systems
    |July 30, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a robust fault-aware Extreme Learning Machine (ELM) algorithm to improve performance degradation caused by noise and faults. The new algorithm demonstrates superior robustness and generalization across various datasets and conditions.

    More Related Videos

    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

    Related Experiment Videos

    Last Updated: Sep 13, 2025

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    584
    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

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Neural Networks

    Background:

    • Extreme Learning Machine (ELM) is a powerful tool for universal approximation.
    • Practical ELM performance is often compromised by weight noise, node faults, and outliers.
    • Existing ELM algorithms lack sufficient robustness in real-world, imperfect conditions.

    Purpose of the Study:

    • To develop a robust Extreme Learning Machine (ELM) algorithm resilient to noise and faults.
    • To enhance the network's stability and generalization capabilities under adverse conditions.
    • To introduce a novel objective function integrating outlier resistance for improved network performance.

    Main Methods:

    • Analysis of classic ELM square error considering weight noise and node faults.
    • Integration of the maximum correntropy criterion (MCC) for outlier resistance.
    • Development and convergence proof of the robust fault-aware ELM (RFAELM) algorithm.

    Main Results:

    • The proposed RFAELM algorithm demonstrates significant robustness against various noise and fault levels.
    • Evaluations on eight benchmark datasets confirm the algorithm's superior generalization capabilities.
    • Statistical comparisons show RFAELM outperforms existing robust ELM algorithms.

    Conclusions:

    • The robust fault-aware ELM (RFAELM) effectively addresses performance degradation in ELMs.
    • RFAELM offers enhanced network resilience and generalization, proving its practical applicability.
    • This novel algorithm represents a significant advancement over current robust ELM techniques.