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Related Concept Videos

Radial System Protection01:23

Radial System Protection

Radial systems employ time-delay overcurrent relays to reduce load interruptions. When a fault occurs, the nearest breaker opens first, while upstream breakers remain closed due to longer delay settings. This approach ensures minimal disruption to the rest of the system.
In a radial system with a fault downstream of the third breaker, ideally, only the third breaker will open, isolating the fault and interrupting the load connected beyond it. The second breaker has a longer delay setting,...
Radius of Gyration of an Area01:12

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.
Rectangular and Triangular Pulse Function01:19

Rectangular and Triangular Pulse Function

The unit rectangular pulse function is mathematically represented by a rectangular function centered at the origin with a height of one unit. This function is defined by two parameters: T, which specifies the center location of the pulse along the time axis, and τ, which determines the pulse duration.
For example, consider a rectangular pulse with a 5V amplitude, a 3-second duration, and centered at t=2 seconds. This pulse can be expressed using the rectangular function, written as,
Radical Chain-Growth Polymerization: Overview01:10

Radical Chain-Growth Polymerization: Overview

Chain-growth or addition polymerization is successive addition reactions of monomers with a polymer chain. In radical chain-growth polymerization, the reaction proceeds via a free-radical intermediate. The free radical is formed from radical initiators, which spontaneously generate free radicals by homolytic fission. Organic peroxides (such as dibenzoyl peroxide, as shown in Figure 1) or azo compounds are popular radical initiators. A low concentration ratio of radical initiator to monomer is...
Cylinders in Three-Dimensional Space01:28

Cylinders in Three-Dimensional Space

A cylindrical surface is generated when a two-dimensional profile curve is translated along a straight line in three-dimensional space. The translated copies of the curve form a surface composed of parallel rulings, each oriented in the same fixed direction. This construction allows many three-dimensional forms to be described using relatively simple planar equations.In Cartesian coordinates, a cylindrical surface is often recognized by an equation that omits one of the three variables. For...
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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Related Experiment Videos

Radial basis function networks GPU-based implementation.

Andreas Brandstetter1, Alessandro Artusi

  • 1Institute of Computer Graphics and Algorithms, Vienna University of Technology, Vienna A1040, Austria.

IEEE Transactions on Neural Networks
|December 5, 2008
PubMed
Summary
This summary is machine-generated.

This study presents a graphic processing unit (GPU) implementation for accelerating radial basis function network (RBFN) training. The GPU approach significantly reduces computational costs compared to central processing unit (CPU) methods.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Computational Science
  • Hardware Acceleration

Background:

  • Neural networks (NNs) offer great potential but face limitations, notably long training times.
  • Traditional hardware implementations (CPU) for NNs are costly and inflexible.
  • Graphic Processing Units (GPUs) have emerged as powerful, versatile computing resources.

Purpose of the Study:

  • To accelerate the training process of Radial Basis Function Networks (RBFNs).
  • To overcome the time performance limitations of RBFNs in dynamic application domains.
  • To demonstrate the efficacy of GPU implementation for RBFN learning.

Main Methods:

  • Developed a complete graphic processing unit (GPU) implementation for the RBFN learning process.
  • Compared the computational cost and training time against traditional central processing unit (CPU) implementations.
  • Focused on optimizing the entire learning procedure for parallel processing on GPUs.

Main Results:

  • Achieved a reduction in computational cost by approximately two orders of magnitude.
  • Demonstrated significant acceleration of the RBFN training process using GPU acceleration.
  • Validated the feasibility and efficiency of GPU-based RBFN learning.

Conclusions:

  • GPU implementation drastically reduces RBFN training time and computational cost.
  • This approach enhances the applicability of RBFNs in time-sensitive domains.
  • Accelerated RBFN learning on GPUs offers a practical solution for complex computational tasks.