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

427
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,...
427
State Space Representation01:27

State Space Representation

744
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...
744
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

1.5K
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...
1.5K
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

8.8K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
8.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

342
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
342
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

5.7K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
5.7K

You might also read

Related Articles

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

Sort by
Same author

Q-Learning Approach to Finite-Horizon H<sub>∞</sub> Tracking With Partial Observation.

IEEE transactions on cybernetics·2026
Same author

Nonlinear Optimal Control Based on FBDEs and its Application to AGV.

IEEE transactions on cybernetics·2024
Same author

Stabilization of Networked Switched Systems Under DoS Attacks.

IEEE transactions on cybernetics·2024
Same author

Q-Learning for Feedback Nash Strategy of Finite-Horizon Nonzero-Sum Difference Games.

IEEE transactions on cybernetics·2021
Same author

Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering.

Sensors (Basel, Switzerland)·2021
Same author

Fingerprinting-Based Indoor Localization Using Interpolated Preprocessed CSI Phases and Bayesian Tracking.

Sensors (Basel, Switzerland)·2020

Related Experiment Video

Updated: Apr 18, 2026

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

4.3K

Distributed weighted least-squares estimation with fast convergence for large-scale systems.

Damián Edgardo Marelli1, Minyue Fu2

  • 1School of Electrical Engineering and Computer Science, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia ; Acoustics Research Institute, Austrian Academy of Sciences, Austria.

Automatica : the Journal of IFAC, the International Federation of Automatic Control
|February 3, 2015
PubMed
Summary

This study introduces distributed algorithms for weighted least-squares estimation in large-scale systems. These methods enable subsystems to compute optimal parameter estimates using local measurements and network communication, enhancing distributed estimation accuracy.

Keywords:
Distributed estimationDistributed state estimationLarge scale optimizationNetworked controlSensor network

More Related Videos

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.9K

Related Experiment Videos

Last Updated: Apr 18, 2026

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

4.3K
Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.9K

Area of Science:

  • Distributed Systems
  • Estimation Theory
  • Networked Control Systems

Background:

  • Large-scale systems often comprise interconnected subsystems, each with partial information.
  • Accurate parameter estimation is crucial for effective system control and analysis.
  • Existing centralized methods are often infeasible for large-scale, distributed networks.

Purpose of the Study:

  • To develop a fully distributed iterative algorithm for weighted least-squares estimation.
  • To enhance the convergence rate of the distributed estimation algorithm.
  • To propose an alternative algorithm for acyclic networks with finite-step convergence.

Main Methods:

  • A fully distributed iterative algorithm is proposed for general networks.
  • Optimization of convergence rate using a scaling parameter and preconditioning.
  • A distinct iterative algorithm is presented for networks without loops.

Main Results:

  • The proposed distributed iterative algorithm asymptotically computes the globally optimal estimate.
  • The algorithm's convergence rate is shown to be maximized through specific optimization techniques.
  • An alternative algorithm achieves finite-step convergence for acyclic networks.

Conclusions:

  • The developed distributed algorithms effectively address weighted least-squares estimation in large-scale systems.
  • The methods enable decentralized computation of optimal parameter estimates through neighborhood communication.
  • Numerical experiments validate the performance and efficiency of the proposed distributed estimation techniques.