Improved probabilistic neural network PNN and its application to defect recognition in rock bolts

The probabilistic neural network (PNN) model is developed based on Bayesian criteria of multivariate pattern classification. Because PNN has the advantages of low training complexity, high stability, quick convergence, and simple construction, it can be well applied in the area of defect recognition...

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Bibliographic Details
Published inInternational journal of machine learning and cybernetics Vol. 7; no. 5; pp. 909 - 919
Main Authors Sun, Xiao-yun, Kang, Feng-ning, Wang, Ming-ming, Bian, Jian-peng, Cheng, Jiu-long, Zou, D. H.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2016
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Classification
Complex Systems
Computational Intelligence
Control
Defects
Discriminant analysis
Eigenvectors
Energy
Engineering
Evolutionary algorithms
Evolutionary computation
Mathematical models
Mechatronics
Methods
Neural networks
Nondestructive testing
Original Article
Parameters
Pattern classification
Pattern Recognition
Recognition
Robotics
Rock bolts
Smoothing
Systems Biology
Wavelet transforms
Improved PNN
Defect recognition
Rock bolts
Wavelet packet
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Summary:The probabilistic neural network (PNN) model is developed based on Bayesian criteria of multivariate pattern classification. Because PNN has the advantages of low training complexity, high stability, quick convergence, and simple construction, it can be well applied in the area of defect recognition. The application of PNN has a bottleneck problem; that is, the selection of smoothing parameters will seriously affect PNN recognition accuracy. In this paper, we propose an improved PNN model that employs a differential evolution algorithm to optimize the smoothing parameters. Furthermore, a defect recognition approach is proposed based on the improved PNN, which is successfully applied to an anchoring testing field. The proposed approach includes two key steps. The first step involves extracting the energy eigenvector of the defect signal with normalization based on a wavelet packet. The second step involves recognizing defects based on the improved PNN. Simulation results show that our improved PNN model is superior to the traditional PNN model. Thus, the improved PNN can provide useful references for recognizing the defect type of rock bolts in engineering.
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-016-0506-2