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

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

47
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...
47
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

657
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
657
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

556
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
556
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

3.7K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
3.7K
Equation of Motion: General Plane motion - Problem Solving01:16

Equation of Motion: General Plane motion - Problem Solving

174
Consider a lawn roller with a mass of 100 kg, a radius of 0.2 meters, and a radius of gyration of 0.15 meters. A force of 200 N is applied to this roller, angled at 60 degrees from the horizontal plane. What will be the angular acceleration of the lawn roller?
The friction between the roller and the ground is characterized by two coefficients. The static friction coefficient is 0.15, while the kinetic friction coefficient is 0.1. These values are crucial in understanding the interaction between...
174
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

369
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
369

You might also read

Related Articles

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

Sort by
Same author

High and low body mass index increases the risk of short-term postoperative complications following total shoulder arthroplasty.

JSES international·2025
Same author

Beam steering by two vertically faced metasurfaces using polarization free unit cells with three operating modes.

Scientific reports·2025
Same author

Identifying risk factors for 30-day readmission after outpatient total shoulder arthroplasty to aid in patient selection.

JSES international·2023
Same author

Solving time delay fractional optimal control problems via a Gudermannian neural network and convergence results.

Network (Bristol, England)·2023
Same author

Abnormal preoperative platelet count may predict postoperative complications following shoulder arthroplasty.

JSES international·2022
Same author

Chronic steroid use and readmission following total shoulder arthroplasty.

JSES international·2022

Related Experiment Video

Updated: Jun 17, 2025

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

8.8K

Solving general convex quadratic multi-objective optimization problems via a projection neurodynamic model.

Mohammadreza Jahangiri1, Alireza Nazemi1

  • 1Faculty of Mathematical Sciences, Shahrood University of Technology, P.O. Box 3619995161- 316, Shahrood, Iran.

Cognitive Neurodynamics
|August 6, 2024
PubMed
Summary

This study introduces a stable neural network model to find optimal solutions for convex quadratic multi-objective programming problems (CQMPP). The method efficiently identifies Pareto optimal solutions using weighted sums and projection networks.

Keywords:
ConvergenceConvex quadratic programming problemMulti-objective optimization problemNeural networksPareto optimal solutionStability

More Related Videos

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.7K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

Related Experiment Videos

Last Updated: Jun 17, 2025

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

8.8K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.7K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

Area of Science:

  • Optimization
  • Artificial Intelligence
  • Applied Mathematics

Background:

  • Convex quadratic multi-objective programming problems (CQMPP) are complex optimization challenges.
  • Finding Pareto optimal solutions (POS) requires efficient computational methods.

Purpose of the Study:

  • To develop a stable and globally convergent neural network model for solving CQMPP.
  • To efficiently identify Pareto optimal solutions by diversifying weight values.

Main Methods:

  • The CQMPP is transformed into a single-objective problem using the weighted sum method.
  • Multiple projection neural networks are employed to search for Pareto optimal solutions.
  • Lyapunov theory is utilized to establish the stability and global convergence of the neural network approach.

Main Results:

  • The proposed neural network model demonstrates stability in the sense of Lyapunov.
  • The model is proven to be globally convergent to the exact optimal solution of the single-objective problem.
  • Simulation results confirm the feasibility and efficiency of the presented neural network approach.

Conclusions:

  • The developed neural network model offers a robust and efficient method for solving CQMPP.
  • The approach provides a reliable way to obtain Pareto optimal solutions for complex optimization tasks.
  • The theoretical foundation based on Lyapunov stability ensures the reliability of the optimization results.