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

Reducing Line Loss01:18

Reducing Line Loss

264
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
264
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

185
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...
185
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

652
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
652
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.6K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.6K
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

422
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
422
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

940
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...
940

You might also read

Related Articles

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

Sort by
Same author

Generalized Maximum Complex Correntropy Augmented Adaptive IIR Filtering.

Entropy (Basel, Switzerland)·2022
Same author

Complex Correntropy with Variable Center: Definition, Properties, and Application to Adaptive Filtering.

Entropy (Basel, Switzerland)·2020
Same author

A Robust Adaptive Filter for a Complex Hammerstein System.

Entropy (Basel, Switzerland)·2020
Same author

Kernel Risk-Sensitive Mean <i>p</i>-Power Error Algorithms for Robust Learning.

Entropy (Basel, Switzerland)·2020
Same author

Electro-Acupuncture Ameliorated MPTP-Induced Parkinsonism in Mice via TrkB Neurotrophic Signaling.

Frontiers in neuroscience·2019
Same author

Enzyme characterization and biological activities of a resuscitation promoting factor from an oil degrading bacterium <i>Rhodococcus erythropolis</i> KB1.

PeerJ·2019
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 27, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.4K

Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm.

Guobing Qian1,2, Dan Luo1, Shiyuan Wang1

  • 1College of Electronic and Information Engineering, Brain-inspired Computing & Intelligent Control of Chongqing Key Laboratory, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing 400715, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

A new recursive algorithm, recursive minimum complex kernel risk-sensitive loss (RMCKRSL), effectively handles complex-valued data with impulsive noise. Simulations show RMCKRSL outperforms existing methods like MCCC and RLS.

Keywords:
EMSEcomplexkernel risk-sensitive lossrecursivestability

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K
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

2.0K

Related Experiment Videos

Last Updated: Nov 27, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K
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

2.0K

Area of Science:

  • Signal processing
  • Machine learning
  • Complex-valued data analysis

Background:

  • The maximum complex correntropy criterion (MCCC) addresses complex-valued data with impulsive noise.
  • Kernel risk-sensitive loss (KRSL) shows superior performance in the complex domain compared to correntropy-based loss.
  • Recursive algorithms for complex KRSL are currently unreported.

Purpose of the Study:

  • To propose a novel recursive complex KRSL algorithm for complex-valued signal processing.
  • To analyze the stability and theoretical excess mean square error (EMSE) of the proposed algorithm.
  • To evaluate the performance of the new algorithm against established methods.

Main Methods:

  • Development of the recursive minimum complex kernel risk-sensitive loss (RMCKRSL) algorithm.
  • Theoretical analysis of algorithm stability and derivation of the EMSE.
  • Comparative simulations against MCCC, generalized MCCC (GMCCC), and recursive least squares (RLS).

Main Results:

  • The proposed RMCKRSL algorithm demonstrates robust performance in the complex domain.
  • Stability analysis confirms the theoretical soundness of the RMCKRSL algorithm.
  • Simulations validate that RMCKRSL significantly outperforms MCCC, GMCCC, and RLS.

Conclusions:

  • The RMCKRSL algorithm is a novel and effective solution for complex-valued data processing in impulsive noise.
  • The proposed method offers improved performance over existing criteria, particularly in recursive applications.
  • This work introduces a valuable tool for advanced signal processing and machine learning tasks involving complex data.