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

Active Filters01:25

Active Filters

1.1K
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
1.1K
Second Order systems II01:18

Second Order systems II

279
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
279
State Space Representation01:27

State Space Representation

389
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...
389
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
Root Loci for Positive-Feedback Systems01:23

Root Loci for Positive-Feedback Systems

220
The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
The construction rules for the root locus in positive feedback systems are similar to those in...
220
Design Example01:23

Design Example

442
The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
442

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

Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm.

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

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

A Robust Adaptive Filter for a Complex Hammerstein System.

Guobing Qian1,2, Dan Luo1, Shiyuan Wang1

  • 1College of Electronic and Information Engineering, 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.

The new Hammerstein maximum complex correntropy criterion (HMCCC) algorithm offers robust performance for complex-valued data, outperforming traditional methods in impulsive noise environments.

Keywords:
Hammersteinadaptive filterscompleximpulsive noisestability

More Related Videos

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

14.0K
Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.4K

Related Experiment Videos

Last Updated: Nov 27, 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

2.0K
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

14.0K
Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.4K

Area of Science:

  • Signal Processing
  • Adaptive Filtering
  • Robust Statistics

Background:

  • Traditional adaptive filters often rely on mean square error (MSE), which is sensitive to outliers.
  • Hammerstein adaptive filters offer improved performance but lack robustness in complex domains.
  • Existing robust methods do not adequately address complex-valued data challenges.

Purpose of the Study:

  • To develop a robust Hammerstein adaptive filter for complex-valued data using the maximum correntropy criterion (MCC).
  • To introduce the Hammerstein maximum complex correntropy criterion (HMCCC) algorithm for handling complex data directly.
  • To analyze the stability and performance of the proposed HMCCC algorithm.

Main Methods:

  • Development of the Hammerstein maximum complex correntropy criterion (HMCCC) algorithm.
  • Extension of the maximum correntropy criterion (MCC) to the complex domain for Hammerstein structures.
  • Analysis of algorithm stability and steady-state mean square performance.
  • Simulations in impulsive noise environments.

Main Results:

  • The HMCCC algorithm demonstrates convergence in impulsive noise.
  • HMCCC achieves higher accuracy compared to the Hammerstein complex least mean square (HCLMS) algorithm.
  • The proposed algorithm exhibits a faster convergence speed than HCLMS.
  • The HMCCC filter effectively handles complex-valued data directly.

Conclusions:

  • The HMCCC algorithm provides a robust solution for adaptive filtering of complex-valued data in non-Gaussian noise.
  • HMCCC offers superior performance in terms of accuracy and convergence speed over existing complex domain methods.
  • This work extends robust adaptive filtering techniques to the complex domain for Hammerstein systems.