16 September 2002 Parameter recognition of steel plate nondestructive testing based on fuzzy neural network
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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.
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Haidong Zhang, Haidong Zhang, Kangsheng Lai, Kangsheng Lai, Dongming Dai, Dongming Dai, } "Parameter recognition of steel plate nondestructive testing based on fuzzy neural network", Proc. SPIE 4929, Optical Information Processing Technology, (16 September 2002); doi: 10.1117/12.483252; https://doi.org/10.1117/12.483252
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