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 Frequency Domain01:26

Linear Approximation in Frequency Domain

314
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....
314
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

287
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,...
287
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

239
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,...
239
Nonlinear Pharmacokinetics: Causes of Nonlinearity01:22

Nonlinear Pharmacokinetics: Causes of Nonlinearity

626
Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
626
Feedback control systems01:26

Feedback control systems

639
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...
639
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

238
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...
238

You might also read

Related Articles

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

Sort by
Same author

<b>Redescription of <i>Agrilus zanthoxylumi</i> Hou & Feng, 1989 (Coleoptera: Buprestidae) based on newly designated neotype</b>.

Zootaxa·2026
Same author

Amplification refractory mutation system polymerase chain reaction-capillary electrophoresis's applicability for newborn screening in dried blood spots of spinal muscular atrophy.

Journal of the Formosan Medical Association = Taiwan yi zhi·2026
Same author

Ultra-high-field 7T MRI reveals neural abnormalities of attention networks in relation to cognitive impairment in hypertension.

Brain research·2026
Same author

Anisodamine Hydrobromide Ameliorates Pulmonary Microcirculatory Dysfunction in Septic Rats.

Microcirculation (New York, N.Y. : 1994)·2026
Same author

Biocontrol potential of the native parasitoid Cotesia gregalis (Hymenoptera: Braconidae) against the invasive fall webworm (Lepidoptera: Erebidae): Insights from reproductive biology and release strategies.

Pest management science·2026
Same author

A bibliometric analysis of the role of apoptosis in breast cancer immunotherapy from 1994 to 2024.

Discover oncology·2026

Related Experiment Video

Updated: Dec 30, 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

2.1K

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model.

Yu-Ting Bai1,2, Xiao-Yi Wang1,2, Xue-Bo Jin1,2

  • 1School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China.

Sensors (Basel, Switzerland)
|January 18, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neuron-based Kalman filter to enhance data sensing precision in intelligent terminals. This adaptive filtering method effectively eliminates noise, improving control system performance.

Keywords:
kalman filterneural networknoise filteringnonlinear autoregressive

More Related Videos

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.9K
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.7K

Related Experiment Videos

Last Updated: Dec 30, 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

2.1K
A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.9K
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.7K

Area of Science:

  • Intelligent control systems
  • Soft computing
  • Signal processing

Background:

  • Data sensing precision is crucial for intelligent terminal control.
  • Traditional Kalman filters struggle with practical system recognition and parameter estimation.
  • Filtering methods are typical soft computing solutions to enhance sensing levels.

Purpose of the Study:

  • To propose a neuron-based Kalman filter to overcome limitations of traditional methods.
  • To optimize the filtering process for improved data sensing precision.
  • To develop an adaptive filtering algorithm for intelligent terminals.

Main Methods:

  • Designed a Kalman filter framework incorporating neuro units.
  • Utilized a nonlinear autoregressive model to define neuro unit functions.
  • Developed an adaptive filtering algorithm based on the enhanced Kalman filter.

Main Results:

  • Neuro units optimized the filtering process, reducing reliance on unpractical models and hypothetical parameters.
  • The proposed filter demonstrated effectiveness in noise elimination.
  • Verification with simulation signals and practical measurements confirmed the filter's efficacy.

Conclusions:

  • The neuron-based Kalman filter significantly improves data sensing precision.
  • The adaptive filtering algorithm offers a robust soft computing solution for intelligent terminals.
  • This approach enhances the control effect of intelligent terminals by improving sensing accuracy.