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 time-invariant Systems01:23

Linear time-invariant Systems

1.1K
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...
1.1K
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

1.1K
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
1.1K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

423
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,...
423
Classification of Systems-II01:31

Classification of Systems-II

565
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
565
Prediction Intervals01:03

Prediction Intervals

3.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.5K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

434
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....
434

You might also read

Related Articles

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

Sort by
Same author

Nanotechnology used for siRNA delivery for the treatment of neurodegenerative diseases: Focusing on Alzheimer's disease and Parkinson's disease.

International journal of pharmaceutics·2024
Same author

Serum apolipoprotein H determines ferroptosis resistance by modulating cellular lipid composition.

Cell death & disease·2024
Same author

Unraveling the Genetic Control of Pigment Accumulation in Physalis Fruits.

International journal of molecular sciences·2024
Same author

New Meroterpenes from South China Sea Soft Coral <i>Litophyton brassicum</i>.

Marine drugs·2024
Same author

Frontier role of extracellular vesicles in kidney disease.

Journal of nanobiotechnology·2024
Same author

Exosomal miR-4745-5p/3911 from N2-polarized tumor-associated neutrophils promotes gastric cancer metastasis by regulating SLIT2.

Molecular cancer·2024

Related Experiment Video

Updated: Apr 5, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

689

Distributed Interval Estimation for Continuous-Time Linear Systems Based on Robust Observer and Interval Analysis.

Jiahui Zhang, Zhenhua Wang, Nacim Meslem

    IEEE Transactions on Cybernetics
    |April 3, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new distributed interval estimation method for continuous-time linear time-invariant (LTI) systems. The approach enhances interval accuracy for system state estimation in networked environments.

    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

    2.2K
    Interactive and Visualized Online Experimentation System for Engineering Education and Research
    08:35

    Interactive and Visualized Online Experimentation System for Engineering Education and Research

    Published on: November 24, 2021

    3.1K

    Related Experiment Videos

    Last Updated: Apr 5, 2026

    Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
    06:44

    Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

    Published on: September 23, 2025

    689
    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

    2.2K
    Interactive and Visualized Online Experimentation System for Engineering Education and Research
    08:35

    Interactive and Visualized Online Experimentation System for Engineering Education and Research

    Published on: November 24, 2021

    3.1K

    Area of Science:

    • Control Systems Engineering
    • Applied Mathematics
    • Networked Systems

    Background:

    • Distributed estimation is crucial for networked systems.
    • Accurate state estimation in continuous-time linear time-invariant (LTI) systems presents challenges.
    • Existing interval observers may lack precision in networked scenarios.

    Purpose of the Study:

    • To develop a novel distributed interval estimation method for continuous-time LTI systems.
    • To improve the tightness of estimated state intervals compared to existing approaches.
    • To leverage robust observer design and interval analysis for enhanced accuracy.

    Main Methods:

    • A two-step approach combining robust observer design and interval analysis.
    • Design of a distributed observer using an $H_{\infty } $ approach for point-valued estimation.
    • Set-valued analysis of estimation error dynamics for interval-valued estimation.
    • Application of elimination by inconsistency for tighter interval characterization.

    Main Results:

    • The proposed method achieves accurate point-valued and reliable interval-valued estimations.
    • Enhanced tightness of estimated state intervals compared to current distributed interval observer methods.
    • Simulation results validate the effectiveness of the developed interval estimation technique.

    Conclusions:

    • The novel distributed interval estimation method offers superior performance for LTI systems.
    • The integration of robust observers and interval analysis effectively improves state estimation accuracy.
    • This work provides a valuable tool for state estimation in distributed and networked systems.