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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

110
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,...
110
Linear time-invariant Systems01:23

Linear time-invariant Systems

313
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
313
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

100
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,...
100
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

88
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...
88
Difference Equation Solution using z-Transform01:24

Difference Equation Solution using z-Transform

339
The z-transform is a powerful tool for analyzing practical discrete-time systems, often represented by linear difference equations. Solving a higher-order difference equation requires knowledge of the input signal and the initial conditions up to one term less than the order of the equation.
The z-transform facilitates handling delayed signals by shifting the signal in the z-domain, which corresponds to delaying the signal in the time domain, and advancing signals by similarly shifting in the...
339
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

120
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
120

You might also read

Related Articles

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

Sort by
Same author

Polarization-controlled optical logic operations in multimode fibers.

Optics express·2026
Same author

Reconfigurable chiroptical metasurface sensors enabled by bound states in the continuum.

iScience·2026
Same author

Development and internal-external validation of a nomogram for predicting postoperative 30-day malnutrition risk in cervical cancer patients: a retrospective cohort study.

American journal of cancer research·2026
Same author

ABHD17C-Mediated S-Depalmitoylation of BCL6B Enhances CD24 Transcription to Resist Macrophage Phagocytosis in Pancreatic Cancer.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Multi-scale remote sensing monitoring of aboveground vegetation carbon storage in long-distance expressways.

Carbon balance and management·2026
Same author

Electric-field-induced electro-optic sideband generation on the silicon platform.

Optics letters·2026

Related Experiment Video

Updated: Aug 4, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

Inverse-Free DZNN Models for Solving Time-Dependent Linear System via High-Precision Linear Six-Step Method.

Min Yang, Yunong Zhang, Haifeng Hu

    IEEE Transactions on Neural Networks and Learning Systems
    |April 4, 2023
    PubMed
    Summary

    A novel inverse-free discrete ZNN model, DZNN-LSS, is developed for time-dependent linear systems (TDLS). This model utilizes a new seventh-order accurate discretization method, significantly enhancing precision for TDLS and related applications.

    More Related Videos

    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

    1.7K
    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    1.2K

    Related Experiment Videos

    Last Updated: Aug 4, 2025

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.3K
    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

    1.7K
    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    1.2K

    Area of Science:

    • Numerical Analysis
    • Computational Mathematics
    • Control Systems Engineering

    Background:

    • Time-dependent linear systems (TDLS) are prevalent in scientific research and practical applications.
    • Conventional methods for solving TDLS often rely on inverse-need models.
    • Existing discrete models may lack sufficient precision for complex TDLS problems.

    Purpose of the Study:

    • To develop an inverse-free continuous Zeroing Neural Network (CZNN) model for TDLS.
    • To propose a novel, high-order accurate discretization method for practical discrete model implementation.
    • To enhance the precision and applicability of discrete ZNN models for TDLS.

    Main Methods:

    • Development of an inverse-free continuous ZNN model by applying the ZNN method twice.
    • Introduction of a general linear six-step (LSS) method with seventh-order precision and five variable parameters.
    • Theoretical analysis of parameter constraints for the LSS method and development of specific LSS variants.
    • Integration of the LSS method to create the discrete ZNN-LSS (DZNN-LSS) model.

    Main Results:

    • The proposed DZNN-LSS model demonstrates significantly improved precision compared to conventional discrete models.
    • Theoretical analyses confirm the efficacy of the general LSS method and the DZNN-LSS model.
    • A specific TDLS example validates the effectiveness and superiority of the DZNN-LSS model.
    • Applications in manipulator control and sound source localization illustrate the model's broad applicability.

    Conclusions:

    • The DZNN-LSS model offers a precise and efficient inverse-free solution for time-dependent linear systems.
    • The novel general LSS discretization method provides a robust foundation for high-accuracy discrete ZNN models.
    • The DZNN-LSS model shows strong potential for real-world applications in robotics and signal processing.