Neural-network-based adaptive sampling of three-dimensional-object surface elastic properties

The paper discusses an adaptive-sampling technique for dimensionality reduction of the set of probing points in the measurement of nonuniform elastic properties of three-dimensional (3-D) objects. Two self-organizing neural-network architectures are compared for this purpose: the neural-gas network...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2006-04, Vol.55 (2), p.483-492
Main Authors: Cretu, A.-M., Petriu, E.M.
Format: Article
Language:English
Subjects:
Adaptive sampling
Adaptive systems
Application software
Computer aided manufacturing
Design automation
Dimensional measurements
Elastic constants
Elasticity
Industrial training
Information technology
Instrumentation
Networks
Neural networks
neural-network applications
Nonuniform
Probes
Reduction
robot tactile systems
Robotic assembly
Robots
Sampling methods
self-organizing feature maps
topology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The paper discusses an adaptive-sampling technique for dimensionality reduction of the set of probing points in the measurement of nonuniform elastic properties of three-dimensional (3-D) objects. Two self-organizing neural-network architectures are compared for this purpose: the neural-gas network and the Kohonen self-organizing map (SOM).
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2006.870114