ISSN:
1433-3058
Keywords:
Bayesan interpretation
;
Classification
;
Neural networks
;
Thermal Non-destructive evaluation
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mathematics
Notes:
Abstract A methodological study on the use of neural networks for defect characterisation by means of a thermal method is presented. Neural networks are used here as defect classifiers, based on the infrared emission of the target object after heating. In this kind of application, there is a high degree of uncertainty in defect class boundaries due to several factors, such as the noise in the measurement, the uneven heating of the target object and the anisotropies in its thermal conductivity. For this reason, the classical ‘1 of N’ coding scheme during training did not provide satisfactory results. Much better results have instead been obtained using a smoother activation function for the output units during training. The non-destructive evaluation of material using neural networks proved extremely satisfactory, especially when compared to the classical procedures of thermographic analysis.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01413826
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