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Abstract: The basal model of directional basis probabilistic neural
network (DBPNN) and the corresponding algorithm and theory applied in
pattern recognition are investigated aiming at utilizing the advantage
and overcoming the shortcomings of directional basis function neural
network (DBFNN) and probabilistic neural network (PNN). Its application
to pattern recognition is from the results obtained in classification of
cracks and porosity in weld defect. It can be seen that DBPNN has
greater improvement in computation speed and classification, compared
with the DBFNN and PNN.
Key words: Directional basis function neural network Probabilistic
neural network Fusion Pattern recognition Directional basis
probabilistic neural network
CLC No: TP183
广东省科技计划基金资助项目(2004A11303001).
Received 20050311, received in revised form 20050818
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