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

295
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...
295
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

201
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
201
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

198
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...
198
Multimachine Stability01:25

Multimachine Stability

389
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
389
Classification of Systems-I01:26

Classification of Systems-I

468
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
468
Feedback control systems01:26

Feedback control systems

586
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...
586

You might also read

Related Articles

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

Sort by
Same author

Pyrene-Fused Xanthone Derivatives: Synthesis, Emission Properties and Stimuli-Responsive Behavior.

Organic letters·2026
Same author

Magnetic resonance imaging radiomic phenotypes stratify response and define immune states in very high-risk, nonmuscle-invasive bladder cancer treated with tislelizumab plus nanoparticle albumin-bound paclitaxel.

Cancer·2026
Same author

Overcoming host immune responses to an AAV-delivered HIV-1 bNAb in rhesus macaques mediated by co-delivery of PD-L1.

bioRxiv : the preprint server for biology·2026
Same author

Co-delivered PD-L1 rescues the protective efficacy mediated by an AAV-expressed HIV-1 bNAb.

bioRxiv : the preprint server for biology·2026
Same author

Defect engineering of VN nanowires enables dual-mode SERS-colorimetric quantification of glutathione in serum.

Nanoscale·2026
Same author

Identifying a Critical Blind Spot: How Commercial AI (CAD) Systems Fail to Detect Faint Ground-Glass Opacities at -730 HU on Low-Dose CT.

Diagnostics (Basel, Switzerland)·2026

Related Experiment Video

Updated: Dec 5, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.0K

Multi-Output Selective Ensemble Identification of Nonlinear and Nonstationary Industrial Processes.

Tong Liu, Sheng Chen, Shan Liang

    IEEE Transactions on Neural Networks and Learning Systems
    |October 14, 2020
    PubMed
    Summary

    This study introduces a novel multi-output selective ensemble regression (SER) for real-time industrial process identification. It efficiently learns new states and prunes old ones, improving accuracy and reducing computational load.

    More Related Videos

    O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
    06:50

    O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

    Published on: November 8, 2019

    6.9K
    Experimental Methods to Study Human Postural Control
    08:12

    Experimental Methods to Study Human Postural Control

    Published on: September 11, 2019

    9.9K

    Related Experiment Videos

    Last Updated: Dec 5, 2025

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
    06:45

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

    Published on: October 28, 2022

    2.0K
    O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
    06:50

    O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

    Published on: November 8, 2019

    6.9K
    Experimental Methods to Study Human Postural Control
    08:12

    Experimental Methods to Study Human Postural Control

    Published on: September 11, 2019

    9.9K

    Area of Science:

    • Engineering
    • Control Systems
    • Machine Learning

    Background:

    • Biological systems adapt by updating memory, learning new information and discarding outdated knowledge for intelligent decision-making.
    • Online identification of nonlinear, time-varying industrial processes requires adaptive models that can handle evolving system dynamics.

    Purpose of the Study:

    • To propose a multi-output selective ensemble regression (SER) method for online identification of multi-output nonlinear time-varying industrial processes.
    • To develop an adaptive local learning approach for identifying and encoding new process states.
    • To create an effective pruning strategy for removing outdated models to reduce computational complexity.

    Main Methods:

    • An adaptive local learning approach using multi-output hypothesis testing to fit local multi-output linear models.
    • Construction of an online multi-output SER predictor by optimizing combining weights based on a probability metric.
    • Implementation of a pruning strategy to remove outdated local models.

    Main Results:

    • The proposed multi-output SER method demonstrated effectiveness in real-time identification of multi-output nonlinear and nonstationary processes.
    • The method achieved high online identification accuracy compared to benchmark schemes.
    • The pruning strategy successfully reduced computational complexity without sacrificing prediction accuracy.

    Conclusions:

    • The developed multi-output SER provides an effective solution for online identification of complex industrial processes.
    • The adaptive learning and pruning mechanisms enable efficient and accurate real-time process modeling.
    • This approach offers a robust tool for improving decision-making in dynamic industrial environments.