Pattern recognition by fusion of directional basis function and probabilistic neural network
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) andprobabilistic...
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| Veröffentlicht in: | Ji xie gong cheng xue bao 2005-12, Vol.41 (12), p.228-233 |
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| container_end_page | 233 |
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| container_issue | 12 |
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| container_title | Ji xie gong cheng xue bao |
| container_volume | 41 |
| creator | Luo, Xiongbiao Chen, Tiequn Wan, Ying |
| description | 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) andprobabilistic neural network (PNN). Its application to pattern recognition is from theresults obtained in classification of cracks and porosity in weld defect. It can be seenthat DBPNN has greater improvement in computation speed and classification, compared with the DBFNN and PNN. |
| doi_str_mv | 10.3901/JME.2005.12.228 |
| format | Article |
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| identifier | ISSN: 0577-6686 |
| ispartof | Ji xie gong cheng xue bao, 2005-12, Vol.41 (12), p.228-233 |
| issn | 0577-6686 |
| language | chi |
| recordid | cdi_proquest_miscellaneous_29544769 |
| source | Alma/SFX Local Collection |
| title | Pattern recognition by fusion of directional basis function and probabilistic neural network |
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