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 Circuits01:17

Linear Circuits

841
A linear circuit is characterized by its output having a direct proportionality to its input, adhering to the linearity property, which encompasses the principles of homogeneity (scaling) and additivity. Homogeneity dictates that when the input, also referred to as the excitation, is multiplied by a constant factor, the output, known as the response, is correspondingly scaled by the same constant factor. For instance, if the current is multiplied by a constant 'k,' the voltage likewise...
841
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Linear Momentum00:55

Linear Momentum

17.6K
The term momentum is used in various ways in everyday language, most of which are consistent with the precise scientific definition. Generally, momentum implies a tendency to continue on course—to move in the same direction; we tend to speak of sports teams or politicians gaining and maintaining the momentum to win.  Momentum is also associated with great mass and speed and is often considered when talking about collisions. For example, when rugby players collide and fall to the...
17.6K
Linearization and Approximation01:26

Linearization and Approximation

49
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
49
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

83
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
83

You might also read

Related Articles

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

Sort by
Same author

Experimental and numerical studies on corrosion-resistant aluminium foam sandwich panel subject to low-velocity impact.

Scientific reports·2024
Same author

Self-Organizing Type-2 Fuzzy Double Loop Recurrent Neural Network for Uncertain Nonlinear System Control.

IEEE transactions on neural networks and learning systems·2024
Same author

A Simplified Method for Predicting Bond-Slip Behaviour of Ribbed Bars and Threaded Rods Glued in Glulam along the Grain.

Materials (Basel, Switzerland)·2023
Same author

Off-the-shelf CAR-engineered natural killer cells targeting FLT3 enhance killing of acute myeloid leukemia.

Blood advances·2023
Same author

Solving Robotic Trajectory Sequential Writing Problem via Learning Character's Structural and Sequential Information.

IEEE transactions on cybernetics·2022
Same author

Survival of an HLA-mismatched, bioengineered RPE implant in dry age-related macular degeneration.

Stem cell reports·2022
Same journal

Synaptic micromechanics and brain softening as a mechanobiological hypothesis for Alzheimer's disease.

Frontiers in neuroscience·2026
Same journal

The relationship between healthy sleep patterns and the risk of scoliosis: a large prospective cohort study.

Frontiers in neuroscience·2026
Same journal

Dynamic functional reorganization in post-stroke aphasia: a state-of-the-art fMRI review from disease evolution to intervention.

Frontiers in neuroscience·2026
Same journal

Correction: Case Report: A possible novel adult-onset, progressive MAO-A hypofunction.

Frontiers in neuroscience·2026
Same journal

Respiratory modulation of neurophysiology and symptoms in athletes with sports-related concussion: a randomized crossover trial.

Frontiers in neuroscience·2026
Same journal

Impact of C-reactive protein-triglyceride-glucose and systemic immune-inflammation indices on obstructive sleep apnea in older adults with depression.

Frontiers in neuroscience·2026
See all related articles

Related Experiment Video

Updated: Jan 23, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

Robust Adaptive Recurrent Cerebellar Model Neural Network for Non-linear System Based on GPSO.

Jian-Sheng Guan1,2, Shao-Jiang Hong1, Shao-Bo Kang1

  • 1College of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen, China.

Frontiers in Neuroscience
|June 14, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a robust adaptive recurrent cerebellar model articulation controller (RARC) optimized with genetic particle swarm optimization (GPSO) for non-linear systems. The GPSO-RARC system effectively optimizes learning rates, ensuring reliable control and minimal error in complex applications.

Keywords:
GPSO algorithmRARC neural networklearning ratenon-linear systemsrobot manipulator system

More Related Videos

Laser Nanosurgery of Cerebellar Axons In Vivo
09:25

Laser Nanosurgery of Cerebellar Axons In Vivo

Published on: July 28, 2014

8.5K
A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

7.1K

Related Experiment Videos

Last Updated: Jan 23, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K
Laser Nanosurgery of Cerebellar Axons In Vivo
09:25

Laser Nanosurgery of Cerebellar Axons In Vivo

Published on: July 28, 2014

8.5K
A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

7.1K

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Robotics

Background:

  • Non-linear systems present significant control challenges.
  • Adaptive recurrent cerebellar model articulation controllers (RARC) offer a promising approach.
  • Optimizing controller parameters, particularly learning rates, is crucial for performance.

Purpose of the Study:

  • To develop a robust adaptive recurrent cerebellar model articulation controller (RARC) for non-linear systems.
  • To utilize the genetic particle swarm optimization (GPSO) algorithm for optimizing RARC learning rates.
  • To validate the proposed GPSO-RARC control system's effectiveness and reliability.

Main Methods:

  • The RARC neural network is employed as the primary tracking controller.
  • A robust compensation controller is designed to address approximation errors.
  • The steepest descent gradient method and Lyapunov function are used for adaptive law derivation.
  • A hybrid genetic algorithm and particle swarm optimization (GPSO) optimizes RARC learning rates.

Main Results:

  • Numerical simulations on inverted pendulum and robot manipulator systems demonstrate the GPSO-RARC system's effectiveness.
  • The proposed control scheme achieves optimal learning rate parameters.
  • The GPSO-RARC system achieves a minimum root mean square error for non-linear systems.
  • The control scheme is validated as reliable compared to other methods.

Conclusions:

  • The GPSO-RARC control system provides a reliable and effective solution for controlling non-linear systems.
  • Optimizing learning rates via GPSO significantly enhances controller performance.
  • The proposed method demonstrates superior performance in terms of error minimization and parameter optimization.