Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Introduction to Nonlinear Inequalities

Introduction to Nonlinear Inequalities

Linear and nonlinear inequalities are fundamental for analyzing variable relationships and identifying ranges satisfying specific conditions. A linear inequality involves variables raised only to the first power, resulting in a straight-line graph. This line partitions the coordinate plane into two distinct regions: one that satisfies the inequality and one that does not. Each region represents a set of solutions where the linear relationship holds true under the specified constraint.Nonlinear...

Mathematical Modeling: Problem Solving

Mathematical Modeling: Problem Solving

Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

Application of Nonlinear Inequalities

Application of Nonlinear Inequalities

A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality: can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the...

Statically Indeterminate Problem Solving

Statically Indeterminate Problem Solving

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...

Growth Models with Integration: Problem Solving

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...

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

A Lyapunov Optimization-Based Approach to Autonomous Vehicle Local Path Planning.

Sensors (Basel, Switzerland)·2025

Same author

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

Cognitive neurodynamics·2024

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

Search research articles

Related Experiment Videos

A new neural network model for solving random interval linear programming problems.

Ziba Arjmandzadeh¹, Mohammadreza Safi¹, Alireza Nazemi²

¹Department of Mathematics, Semnan University, Semnan, Iran.

Neural Networks : the Official Journal of the International Neural Network Society

|March 4, 2017

Summary

This study introduces a novel neural network model for random interval linear programming. The model ensures stability and global convergence, providing an exact solution for complex optimization problems.

Keywords:

Convergent Convex second order cone programming Neural network Random interval linear programming Satisfactory solution Stability

Related Experiment Videos

Area of Science:

Optimization
Artificial Intelligence
Mathematical Programming

Background:

Linear programming problems with interval coefficients present significant computational challenges.
Existing methods may struggle with the uncertainty inherent in random interval variables.

Purpose of the Study:

To develop a stable and globally convergent neural network model for solving random interval linear programming problems.
To transform interval programming into a solvable convex second-order cone programming problem.

Main Methods:

Transformation of the random interval linear programming problem into a convex second-order cone programming problem.
Construction of a neural network model tailored for the transformed convex problem.
Application of Lyapunov function analysis to prove model stability and convergence.

Main Results:

The proposed neural network model demonstrates Lyapunov stability.
The model achieves global convergence to an exact solution for the original problem.
Illustrative examples confirm the effectiveness of the neural network approach.

Conclusions:

The presented neural network offers an efficient and reliable method for solving random interval linear programming.
The technique provides a robust framework for handling uncertainty in optimization.
This work advances the application of neural networks in mathematical programming.