Application of self-adaptive wavelet neural networks in ultrasonic detecting

Authors:
Xi-Peng Yin;Yang-Yu Fan;Zhe-Min Duan;Wei Cheng
Affiliations:
Department of Electronic Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi, P.R.China;Department of Electronic Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi, P.R.China;Department of Electronic Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi, P.R.China;Department of Electronic Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi, P.R.China
Venue:
ASID'09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication
Year:
2009

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Abstract

It is important to remove the noise signal effectively in non-destructive testing. Using the wavelet and neural network algorithm, the author constructed self-adaptive wavelet neural networks in the ultrasonic testing. Better fitting signal is achieved by choosing Orthogonal Daubechies wavelet neuron and optimized scale parameter. The simulation results showed less distortion and better noise cancellation, and the method can be widely applied ton ultrasonic detecting.