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Proceedings Paper

Parameter recognition of steel plate nondestructive testing based on fuzzy neural network
Author(s): Haidong Zhang; Kangsheng Lai; Dongming Dai
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Paper Abstract

An innovative neruofuzzy network is proposed herein for parameter recognition, specifically for steel plate's defects size inspection through different NDT sources data fusion. A neural network architecture is used to automatically deduce membership function based on a hybrid supervised learning scheme and a set of activation functions are used to adapt to different fuzzy states. The realization of this model and its characteristics are discussed in detail. The application of this model on the inspection of surface defect sizes shows that a quantitative method for determining the actual defect size is successfully developed to make full use of the measured defects sizes from different NDT sources.

Paper Details

Date Published: 16 September 2002
PDF: 9 pages
Proc. SPIE 4929, Optical Information Processing Technology, (16 September 2002); doi: 10.1117/12.483252
Show Author Affiliations
Haidong Zhang, Dalian Univ. of Technology (China)
Kangsheng Lai, Dalian Univ. of Technology (China)
Dongming Dai, Dalian Univ. of Technology (China)

Published in SPIE Proceedings Vol. 4929:
Optical Information Processing Technology
Guoguang Mu; Francis T. S. Yu; Suganda Jutamulia, Editor(s)

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