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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

380
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...
380
Feedback control systems01:26

Feedback control systems

763
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...
763
Effects of feedback01:24

Effects of feedback

1.1K
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
1.1K
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

129
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
129
Linearization and Approximation01:26

Linearization and Approximation

128
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
128
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

381
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,...
381

You might also read

Related Articles

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

Sort by
Same author

Crooks fluctuation theorem for a process on a two-dimensional fluid field.

Physical review letters·2014
Same author

Assessing the performance of dynamical trajectory estimates.

Physical review. E, Statistical, nonlinear, and soft matter physics·2014
Same author

Probabilistic evaluation of time series models: a comparison of several approaches.

Chaos (Woodbury, N.Y.)·2010
Same author

Efficient noncausal noise reduction for deterministic time series.

Chaos (Woodbury, N.Y.)·2003
Same author

When are synchronization errors small?

Physical review. E, Statistical, nonlinear, and soft matter physics·2002

Related Experiment Video

Updated: Mar 12, 2026

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

Assessing the performance of data assimilation algorithms which employ linear error feedback.

Noeleene Mallia-Parfitt1, Jochen Bröcker1

  • 1School of Mathematical, Physical and Computational Sciences, University of Reading, Whiteknights, PO Box 220, Reading RG6 6AX, United Kingdom.

Chaos (Woodbury, N.Y.)
|November 3, 2016
PubMed
Summary

This study introduces a method to realistically assess data assimilation performance by estimating optimism. This approach, inspired by statistical learning, provides a more accurate view of "out of sample" results.

More Related Videos

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

9.1K

Related Experiment Videos

Last Updated: Mar 12, 2026

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
Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

9.1K

Area of Science:

  • Earth System Science
  • Computational Science
  • Statistical Modeling

Background:

  • Data assimilation aims to align dynamical model trajectories with observations.
  • Direct performance evaluation using assimilated data can be overly optimistic.
  • A need exists for realistic assessment of data assimilation model performance.

Purpose of the Study:

  • To develop a method for estimating optimism in data assimilation.
  • To provide a more realistic assessment of "out of sample" performance.
  • To enable performance improvement of data assimilation models.

Main Methods:

  • Inspired by statistical learning techniques for model selection and assessment.
  • Applied statistical learning ideas to data assimilation algorithms.
  • Developed an operationally viable means of assessment for data assimilation.

Main Results:

  • The proposed method provides a realistic estimate of "out of sample" performance.
  • The approach allows for operational viability in assessing data assimilation.
  • Demonstrated performance improvement by optimizing feedback gain in linear feedback data assimilation.

Conclusions:

  • Estimating optimism offers a more accurate evaluation of data assimilation.
  • The method is applicable for improving data assimilation models and algorithms.
  • This technique provides a practical tool for enhancing scientific model performance.