ISSN:
1573-4862
Keywords:
Welds
;
flaw classification
;
ultrasonics
;
neural networks
Source:
Springer Online Journal Archives 1860-2000
Topics:
Electrical Engineering, Measurement and Control Technology
,
Mathematics
Notes:
Abstract A probabilistic neural network is used here to classify flaws in weldments from their ultrasonic scattering signatures. It is shown that such a network is both simple to construct and fast to train. Probabilistic nets are also shown to be able to exhibit the high performance of other neural networks, such as feed forward nets trained via back-propagation, while possessing important advantages of speed, explicitness of their architecture, and physical meaning of their outputs. Probabilistic nets are also demonstrated to have performance equal to common statistical approaches, such as theK-nearest neighbor method, while retaining their unique advantages.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00568290
