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

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

You might also read

Related Articles

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

Sort by
Same author

Crab Shell Inspired Chitin/β-Tricalcium Phosphate Screws as Orthopedic Implants.

Biomacromolecules·2026
Same author

Nuciferine alleviates cerebral ischemia-reperfusion injury by inhibiting mPTP opening via activating the phosphatidylinositol 3-kinase/protein kinase B/glycogen synthase kinase 3 beta pathway.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Bioinspired arrangement of nanohydroxyapatite in chitin matrix for high-strength bone plates.

International journal of biological macromolecules·2026
Same author

Mechanical-electrical-thermal multiphysics modelling and experimental validation of bulk-wave propagation in 316LN stainless steel under cryogenic conditions.

Ultrasonics·2026
Same author

Serotonin syndrome risk with concomitant opioid and serotonergic antidepressant use: a multinational pharmacovigilance study.

Frontiers in pharmacology·2026
Same author

Dynamic hydrogen buffer integrated with single Pt sites for enhanced durability of high-temperature proton exchange membrane fuel cells.

Science bulletin·2026
Same journal

A robust ATUB-Net for bearing fault diagnosis under unbalanced sample scenarios.

ISA transactions·2026
Same journal

Data-driven trajectory tracking control of UAV systems under a novel probability-selection event-triggered mechanism.

ISA transactions·2026
Same journal

Predefined-time affine formation tracking control of unmanned surface vehicles with input saturation via adaptive fuzzy observers.

ISA transactions·2026
Same journal

Adaptive fault-tolerant safety-guaranteed fuzzy event-triggered rendezvous control for heterogeneous USV-UUV systems.

ISA transactions·2026
Same journal

Two-stage maximum likelihood weighted recursive least squares algorithm for nonlinear systems and an application in wind tunnel systems.

ISA transactions·2026
Same journal

Enhancing interpretable soft sensing with embedded hybrid modeling: the GraphTrans approach for industrial processes.

ISA transactions·2026
See all related articles

Related Experiment Video

Updated: Jun 18, 2026

One Dimensional Turing-Like Handshake Test for Motor Intelligence
14:05

One Dimensional Turing-Like Handshake Test for Motor Intelligence

Published on: December 15, 2010

A novel optimization algorithm for MIMO Hammerstein model identification under heavy-tailed noise.

Qibing Jin1, Hehe Wang1, Qixin Su1

  • 1Institute of Automation, Beijing University of Chemical Technology, Beijing 100029, PR China.

ISA Transactions
|October 17, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new Gaussian-Mixture Distribution intelligent optimization algorithm (GMDA) for identifying multi-input multi-output (MIMO) Hammerstein processes, even with heavy-tailed noise. The method effectively models nonlinear systems and overcomes limitations of traditional analytical approaches.

Keywords:
Cuckoo SearchGMDAHeavy-tailed noiseMIMO Hammerstein modelRadial Basis Function neural network

More Related Videos

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors
08:33

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors

Published on: July 28, 2023

Related Experiment Videos

Last Updated: Jun 18, 2026

One Dimensional Turing-Like Handshake Test for Motor Intelligence
14:05

One Dimensional Turing-Like Handshake Test for Motor Intelligence

Published on: December 15, 2010

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors
08:33

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors

Published on: July 28, 2023

Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • System identification of multi-input multi-output (MIMO) Hammerstein processes is challenging, particularly under heavy-tailed noise conditions.
  • Existing analytical methods often struggle with the complexities introduced by heavy-tailed noise in these systems.

Purpose of the Study:

  • To develop a robust and general method for identifying MIMO Hammerstein processes affected by heavy-tailed noise.
  • To address the limitations of current analytical techniques in handling such noise distributions.

Main Methods:

  • A novel Gaussian-Mixture Distribution intelligent optimization algorithm (GMDA) is proposed.
  • The nonlinear dynamics of the Hammerstein process are modeled using a Radial Basis Function (RBF) neural network.
  • A meta-heuristic Cuckoo Search (CS) algorithm, enhanced with Gaussian-Mixture Distribution (GMD) and GMD sequences, is employed to solve the resulting optimization problem.

Main Results:

  • Numerical simulations demonstrate the effectiveness of the proposed GMDA for identifying various MIMO Hammerstein models.
  • The enhanced CS algorithm shows improved performance in handling heavy-tailed noise compared to standard methods.

Conclusions:

  • The developed GMDA provides a viable and effective solution for the system identification of MIMO Hammerstein processes under heavy-tailed noise.
  • The integration of GMD concepts into the Cuckoo Search algorithm enhances its robustness and applicability in challenging noise environments.