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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

310
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...
310
Neural Regulation01:37

Neural Regulation

42.8K
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.
42.8K
Neural Circuits01:25

Neural Circuits

2.4K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.4K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

415
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...
415
Neural Control of Respiration01:18

Neural Control of Respiration

4.2K
The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
4.2K
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

5.6K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
5.6K

You might also read

Related Articles

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

Sort by
Same author

Precision nutrition in diabetic foot ulcers: multimodal artificial intelligence for personalized metabolic management.

Frontiers in nutrition·2026
Same author

Mechanistic Insights and Therapeutic Advances of Anti-Inflammatory Biologics in Immune-Mediated Glomerulonephritis: A Narrative Review.

Journal of inflammation research·2026
Same author

Carbamazepine-Induced Paroxysmal Dysarthria and Ataxia in an Elderly Patient: A Case Report and Clinical Considerations.

Neurotoxicity research·2026
Same author

Metal-polyphenol nanomedicines for malignant tumor therapy.

Frontiers in chemistry·2026
Same author

scTrends: automated classification and strength quantification of gene expression trends along pseudotime in single-cell RNA-seq.

BMC genomics·2026
Same author

Population pharmacokinetic analysis and dosing regimen optimization of eravacycline in critically ill patients with carbapenem-resistant Acinetobacter baumannii pneumonia.

The Journal of antimicrobial chemotherapy·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
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

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

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

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

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

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

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

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

Related Experiment Videos

Automatic Generation Control Based on Multiple Neural Networks With Actor-Critic Strategy.

Lei Xi, Junnan Wu, Yanchun Xu

    IEEE Transactions on Neural Networks and Learning Systems
    |July 15, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a deep reinforcement learning control strategy for automatic generation control (AGC) to manage renewable energy disturbances. The novel TDAC method enhances grid stability and optimizes power system performance.

    Related Experiment Videos

    Area of Science:

    • Electrical Engineering
    • Artificial Intelligence
    • Control Systems

    Background:

    • Conventional automatic generation control (AGC) struggles with the instability caused by high renewable energy integration in power grids.
    • Strong random disturbances from renewable sources necessitate advanced control strategies for grid stability.

    Purpose of the Study:

    • To propose a deep reinforcement learning-based control strategy for AGC to address challenges posed by renewable energy.
    • To enhance the efficiency, quality, and overall control performance of AGC systems.

    Main Methods:

    • Development of a three-network double-delay actor-critic (TDAC) control strategy using deep reinforcement learning.
    • Implementation of a modified actor-critic (AC) method with an incentive heuristic mechanism.
    • Utilizing a novel iterative approach for the value function to minimize optimization bias.

    Main Results:

    • The TDAC strategy demonstrates superior exploratory stability and learning capabilities compared to other smart methods.
    • Simulations confirm improved dynamic performance and effective regional optimal coordinated control of the power grid.
    • The proposed method enhances exploration efficiency and the quality of AGC.

    Conclusions:

    • The TDAC control strategy effectively manages disturbances from renewable energy sources in power grids.
    • This deep reinforcement learning approach offers a robust solution for optimizing power system control and stability.
    • TDAC achieves optimal coordinated control, improving overall grid performance and reliability.