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

Transfer Function in Control Systems01:21

Transfer Function in Control Systems

548
The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
548
Block Diagram Reduction01:22

Block Diagram Reduction

236
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
236
State Space to Transfer Function01:21

State Space to Transfer Function

226
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
226
Transfer Function to State Space01:23

Transfer Function to State Space

287
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
287
Signal Flow Graphs01:18

Signal Flow Graphs

248
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
248
Network Function of a Circuit01:25

Network Function of a Circuit

312
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
312

You might also read

Related Articles

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

Sort by
Same author

Hybrid Nature-Inspired Optimization for the Cell Formation Problem with Machine Reliability and Alternative Routings.

Biomimetics (Basel, Switzerland)·2026
Same author

Metaheuristic-Optimized Convolutional Neural Network for Automated Diagnosis of Viral Pneumonia and Tuberculosis from Chest X-Rays.

Diagnostics (Basel, Switzerland)·2026
Same author

Enhancing Manufacturing Cell Formation Through Availability-Based Optimization Using the Black Widow Optimizer Metaheuristic.

Biomimetics (Basel, Switzerland)·2026
Same author

Evaluating Bio-Inspired Metaheuristics for Dynamic Surgical Scheduling: A Resilient Three-Stage Flow Shop Model Under Stochastic Emergency Arrivals.

Biomimetics (Basel, Switzerland)·2026
Same author

Bioinspired Optimization for Feature Selection in Post-Compliance Risk Prediction.

Biomimetics (Basel, Switzerland)·2026
Same author

A Novel Binary Dream Optimization Algorithm with Data-Driven Repair for the Set Covering Problem.

Biomimetics (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jul 15, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.7K

Binarization of Metaheuristics: Is the Transfer Function Really Important?

José Lemus-Romani1, Broderick Crawford2, Felipe Cisternas-Caneo2

  • 1Escuela de Construcción Civil, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile.

Biomimetics (Basel, Switzerland)
|September 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach for solving binary combinatorial problems using continuous metaheuristics. The research found that specific binarization rules significantly outperform transfer functions, with elite and elite roulette rules proving most effective.

Keywords:
Q-learningbinarization scheme selectiondiversity metricsgrey wolf optimizerset covering problemsine cosine algorithmwhale optimization algorithm

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

Related Experiment Videos

Last Updated: Jul 15, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.7K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization

Background:

  • Binary combinatorial optimization problems are prevalent in various fields.
  • Continuous metaheuristics offer a powerful framework for solving complex optimization tasks.
  • Effective binarization is crucial for adapting continuous metaheuristics to binary problems.

Purpose of the Study:

  • To propose and evaluate an approach for solving binary combinatorial problems using continuous metaheuristics.
  • To investigate the impact of different binarization schemes, including transfer functions and binarization rules, on algorithm performance.
  • To identify optimal strategies for binarization through reinforcement learning-based action selection.

Main Methods:

  • Development of a reinforcement learning-based selector to combine transfer functions and binarization rules.
  • Implementation and testing of various binarization schemes for continuous metaheuristics.
  • Experimental analysis of algorithm performance, focusing on the influence of binarization rules versus transfer functions.
  • Statistical testing and graphical analysis to evaluate exploration and exploitation trade-offs.

Main Results:

  • Binarization rules demonstrated a more significant impact on algorithm performance than transfer functions.
  • Certain sets of actions, particularly those incorporating the elite or elite roulette binarization rule, yielded superior results.
  • Analysis of exploration and exploitation revealed distinct performance characteristics for different action sets.
  • Statistical tests confirmed the superiority of specific action sets for binary combinatorial optimization.

Conclusions:

  • The proposed approach offers a practical method for selecting effective binarization schemes in binary combinatorial optimization.
  • Elite and elite roulette binarization rules are highly recommended for improving continuous metaheuristic performance on binary problems.
  • Further research can build upon these findings to develop more sophisticated binarization strategies and enhance optimization algorithms.