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

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...

Radius of Gyration of an Area

Radius of Gyration of an Area

The second moment of area, also known as the moment of inertia of area, is a crucial factor in understanding an object's resistance against bending deformation, or stiffness. To accurately estimate the second moment of area along any axis, one needs to concentrate all areas associated with that object into a thin strip, which should be placed parallel to that particular axis.

Methods of Medium Optimization

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...

Lagrange Multipliers: Two Constraints

Lagrange Multipliers: Two Constraints

The method of Lagrange multipliers with two constraints is used to optimize a function subject to two independent constraints. In many applications, the objective function represents a quantity to be maximized or minimized, such as cost, area, distance, or energy. The two constraints represent requirements that the solution must satisfy, such as fixed volume, limited resources, or prescribed dimensions.For a function of three variables, each constraint forms a surface in three-dimensional space.

Maximizing the Directional Derivative

Maximizing the Directional Derivative

The directional derivative is a central concept in multivariable calculus that describes how a function changes at a given point when moving in a specified direction. This direction is represented by a unit vector, ensuring that only the orientation influences the rate of change. By varying the direction, different rates of change can be observed, demonstrating that the directional derivative depends strongly on the chosen direction.The directional derivative is computed using the gradient...

Linear Approximations

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...

You might also read

Related Articles

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

Sort by

Same author

B-cell acute lymphoblastic leukemia after lenalidomide maintenance therapy; a deleterious adverse event that needs further investigation. Report of three cases and review of the literature.

Leukemia & lymphoma·2023

Same author

Bisphenol A in the environment and recent advances in biodegradation by fungi.

Chemosphere·2022

Same author

A real-life overview of a hematopoietic cell transplant program throughout a four-year period, including prospective registry, exclusion causes and final donor selection.

Bone marrow transplantation·2021

Same author

Impact of the Inclusion of an Aminoglycoside to the Initial Empirical Antibiotic Therapy for Gram-Negative Bloodstream Infections in Hematological Neutropenic Patients: a Propensity-Matched Cohort Study (AMINOLACTAM Study).

Antimicrobial agents and chemotherapy·2021

Same author

Pexophagy modes during penicillin biosynthesis in Penicillium rubens P2-32-T.

Archives of microbiology·2020

Same author

Verbascoside production in long-term <i>Buddleja cordata</i> Kunth cell suspension cultures.

3 Biotech·2020

Search research articles

Related Experiment Videos

Multiobjective evolutionary optimization of the size, shape, and position parameters of radial basis function

J Gonzalez¹, I Rojas, J Ortega

¹Dept. of Comput. Archit. and Comput. Technol., Univ. of Granada, Spain.

IEEE Transactions on Neural Networks

|February 5, 2008

Summary

This study introduces a novel evolutionary algorithm to optimize radial basis function neural networks (RBFNNs). Global mutation operators based on singular value decomposition (SVD) and orthogonal least squares (OLS) significantly improve RBFNN parameter tuning.

Related Experiment Videos

Area of Science:

Computational Intelligence
Machine Learning
Neural Networks

Background:

Optimizing radial basis function neural networks (RBFNNs) is crucial for approximating complex target functions from input-output data.
Existing evolutionary algorithms may benefit from enhanced operators for more efficient parameter tuning.

Purpose of the Study:

To develop and evaluate a multiobjective evolutionary algorithm for optimizing RBFNNs.
To introduce novel genetic operators inspired by singular value decomposition (SVD) and orthogonal least squares (OLS) for improved network performance.

Main Methods:

A multiobjective evolutionary algorithm was designed to optimize RBFNNs.
New mutation operators were developed using SVD and OLS matrix transformations.
These operators introduce local or global modifications to the radial basis functions (RBFs) within the network's population.

Main Results:

The efficiency of various genetic operators was analyzed.
Global mutation operators demonstrated superior performance in adjusting RBFNN parameters.
The proposed evolutionary approach effectively optimizes RBFNNs for function approximation tasks.

Conclusions:

The integration of SVD and OLS-based mutation operators enhances evolutionary optimization of RBFNNs.
Global mutation operators are particularly effective for fine-tuning RBFNN parameters.
This work provides an improved methodology for developing accurate RBFNN models.