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

Feedback control systems01:26

Feedback control systems

267
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
267
Reinforcement Schedules01:24

Reinforcement Schedules

126
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
126
Control Systems01:10

Control Systems

998
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
998
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

505
Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
505
Operant Conditioning Intervention01:24

Operant Conditioning Intervention

33
Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
33

You might also read

Related Articles

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

Sort by
Same author

Editorial: Deep learning in brain-computer interfaces.

Frontiers in human neuroscience·2026
Same author

Adversarially regularized transformer with channel-wise noise for robust hand gesture recognition using surface electromyography.

Biomedical engineering letters·2026
Same author

Successful Medical Management of Severe Macroglossia Associated With Lingual Abscess in a Dog.

Journal of the American Animal Hospital Association·2026
Same author

A Behind-The-Ear Patch-Type Mental Healthcare Integrated Interface with Adaptive Multimodal Offset Compensation and Parasitic Cancellation.

IEEE transactions on biomedical circuits and systems·2025
Same author

Whole-genome landscapes of 1,364 breast cancers.

Nature·2025
Same author

Cryopreserved Tissue Biospecimens Offer Superior Quality for Whole-Genome Sequencing of Various Cancers Compared to Paired Formalin-Fixed Paraffin-Embedded Tissues.

International journal of molecular sciences·2025
Same journal

Robust Semiglobal and Global Stabilization for Nonlinear Normal Form Systems by Time-Varying Feedback.

IEEE transactions on cybernetics·2026
Same journal

Adaptive Global Asymptotic Output Stabilization of Uncertain Nonlinear Systems Under Dynamic State/Input Quantization.

IEEE transactions on cybernetics·2026
Same journal

Accelerated Distributed Gradient Tracking for Constrained Aggregative Optimization Over Time-Varying Digraphs.

IEEE transactions on cybernetics·2026
Same journal

Small-Gain-Based Plug-and-Play Distributed Control Framework for DC Microgrids With Decentralized Reconfiguration.

IEEE transactions on cybernetics·2026
Same journal

Prescribed-Time Impulsive Control of High-Order Integrator Systems.

IEEE transactions on cybernetics·2026
Same journal

Relaxed Stability Conditions for Model Predictive Control of Hybrid Dynamical Systems Using Hybrid Recurrent Neural Networks.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: May 23, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

472

Intelligent Resilient Security Control for Fractional-Order Multiagent Networked Systems Using Reinforcement Learning

G Narayanan, Rajagopal Karthikeyan, Sangmoon Lee

    IEEE Transactions on Cybernetics
    |March 7, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an intelligent, resilient event-triggered control for fractional-order multiagent networked systems (FOMANSs) using reinforcement learning (RL). It enhances stability and robustness against unknown dynamics and denial-of-service (DoS) attacks.

    More Related Videos

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
    11:54

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

    Published on: May 8, 2021

    4.3K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.2K

    Related Experiment Videos

    Last Updated: May 23, 2025

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    472
    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
    11:54

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

    Published on: May 8, 2021

    4.3K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.2K

    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Networked Systems

    Background:

    • Fractional-order multiagent networked systems (FOMANSs) face challenges with unknown dynamics, actuator faults, and denial-of-service (DoS) attacks.
    • Existing control methods struggle with adaptability and resilience in complex, uncertain environments.

    Purpose of the Study:

    • To develop an intelligent, resilient event-triggered control method for FOMANSs.
    • To address unknown dynamics, actuator faults, and DoS attacks using reinforcement learning (RL).

    Main Methods:

    • Implemented an adaptive learning law with neural networks and fuzzy logic for unknown nonlinear dynamics.
    • Combined RL with sliding mode control for optimized distributed control protocols.
    • Formulated a dual-event-triggered control strategy to mitigate DoS attack impacts.

    Main Results:

    • Achieved robust tracking and guaranteed Mittag-Leffler stability of the closed-loop system.
    • Successfully mitigated the effects of DoS attacks and ensured resilient control.
    • Validated the control strategy on a single-link flexible-joint robotic manipulator system.

    Conclusions:

    • The proposed intelligent, resilient event-triggered control method effectively enhances FOMANS performance.
    • The integration of RL and dual-event-triggering provides a robust solution for networked systems under attack.
    • This approach offers significant advancements in secure and stable control for complex systems.