This paper presents a novel approach for training a multiresolution, hierarchical wavelet basis function neural network. Such a network can be employed for characterizing defects in gas pipelines which are inspected using the magnetic flux leakage method of nondestructive testing. The results indicate that significant advantages over other neural network based defect characterization schemes could be obtained, in that the accuracy of the predicted defect profile can be controlled by the resolution of the network. The centers of the basis functions are calculated using a dyadic expansion scheme and a hybrid learning method. The performance of the network is demonstrated by predicting defect profiles from experimental magnetic flux leakage signals.
|Original language||English (US)|
|Number of pages||4|
|State||Published - Dec 1 1996|
|Event||Proceedings of the 1996 IEEE 39th Midwest Symposium on Circuits & Systems. Part 3 (of 3) - Ames, IA, USA|
Duration: Aug 18 1996 → Aug 21 1996
|Other||Proceedings of the 1996 IEEE 39th Midwest Symposium on Circuits & Systems. Part 3 (of 3)|
|City||Ames, IA, USA|
|Period||8/18/96 → 8/21/96|
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering
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Shreekanth Mandayam (Manager) & George D. Lecakes (Manager)