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

State Space Representation01:27

State Space Representation

461
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
461
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

268
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
268
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
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...
1.6K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.8K
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...
8.8K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.0K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.0K
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

484
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
484

You might also read

Related Articles

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

Sort by
Same author

Mirtazapine alleviates depressive-like behaviors through the paraventricular SIK1-CRTC1 signaling pathway.

Journal of affective disorders·2026
Same author

Core targets of bisphenol A in cervical cancer revealed by network toxicology and molecular docking.

Medicine·2026
Same author

Optimized Distributed Filtering Over Binary Sensor Network: A Dynamic Event-Triggering Protocol With Token Bucket Specifications.

IEEE transactions on cybernetics·2026
Same author

Higher dietary diversity is inversely associated with antenatal anxiety, depression, and their comorbid symptoms: a population-based cross-sectional study in Southwest China.

European journal of nutrition·2026
Same author

Association of dietary choline intake in late pregnancy with the early neurobehavioral development of offspring.

European journal of nutrition·2026
Same author

Force-competition mechanisms and particle dynamics in structurally coupling-regulated cascaded microcavity optical tweezers.

Optics letters·2026
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 Videos

Outlier-Resistant Remote State Estimation for Recurrent Neural Networks With Mixed Time-Delays.

Jiahui Li, Zidong Wang, Hongli Dong

    IEEE Transactions on Neural Networks and Learning Systems
    |May 27, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an outlier-resistant state estimation method for recurrent neural networks (RNNs) with time delays. The new approach effectively handles measurement outliers, ensuring stable estimation performance and H-infinity bounds.

    Related Experiment Videos

    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Recurrent Neural Networks (RNNs) are susceptible to performance degradation due to measurement outliers caused by sensor faults or environmental changes.
    • Mixed time-delays (discrete and distributed) in RNNs complicate state estimation.
    • Existing state estimation methods struggle with the impact of abnormal measurement disturbances.

    Purpose of the Study:

    • To develop a novel outlier-resistant state estimation (SE) strategy for RNNs with mixed time-delays.
    • To mitigate the adverse effects of measurement outliers on estimation error dynamics (EEDs).
    • To ensure asymptotic stability of EEDs with a guaranteed H-infinity performance index.

    Main Methods:

    • A confidence-dependent saturation function is proposed to suppress the influence of measurement outliers.
    • Lyapunov-Krasovskii functional and inequality manipulations are employed for theoretical analysis.
    • A delay-dependent criterion is derived for the existence of the proposed estimator.

    Main Results:

    • An outlier-resistant state estimator for RNNs with mixed time-delays is designed.
    • The asymptotic stability of the estimation error dynamics is guaranteed with a prescribed H-infinity performance.
    • The estimator gain is explicitly characterized via a convex optimization problem.

    Conclusions:

    • The proposed method effectively addresses the outlier-resistant state estimation problem in RNNs with mixed time-delays.
    • Numerical simulations validate the theoretical results and demonstrate the estimator's robustness.
    • The findings contribute to reliable state estimation in the presence of noisy and abnormal measurements.