| 方向基概率神经网络的模式识别 |
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| 引用本文： | 罗雄彪,陈铁群,万英. 方向基概率神经网络的模式识别[J]. 机械工程学报, 2005, 41(12): 228-233 |
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| 作者姓名： | 罗雄彪 陈铁群 万英 |
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| 作者单位： | 华南理工大学机械工程学院,广州,510640;华南理工大学机械工程学院,广州,510640;华南理工大学机械工程学院,广州,510640 |
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| 基金项目： | 广东省科技计划基金资助项目(2004A11303001)。 |
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| 摘 要： | 针对方向基函数神经网络与概率神经网络的优缺点，提出了方向基概率神经网络的基本模型，并讨论了它用作模式分类时的相关原理及算法。以焊缝中裂纹和气孔缺陷的分类为例，用方向基概率神经网络对缺陷进行模式识别。结果表明：该网络应用于模式识别，较方向基函数神经网络和概率神经网络不仅在速度上有较大的提高，而且在分类性能上也有明显的改善。
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| 关 键 词： | 方向基函数神经网络 概率神经网络 融合 模式识别 方向基概率神经网络 |
| 修稿时间： | 2005-03-11 |
PATTERN RECOGNITION BY FUSION OF DIRECTIONAL BASIS FUNCTION AND PROBABILISTIC NEURAL NETWORK |
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| Luo Xiongbiao,Chen Tiequn,Wan Ying. PATTERN RECOGNITION BY FUSION OF DIRECTIONAL BASIS FUNCTION AND PROBABILISTIC NEURAL NETWORK[J]. Chinese Journal of Mechanical Engineering, 2005, 41(12): 228-233 |
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| Authors: | Luo Xiongbiao Chen Tiequn Wan Ying |
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| Affiliation: | College of Mechanical Engineering, South China University of Technology |
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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. |
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| Keywords: | Directional basis function neural network Probabilistic neural network Fusion Pattern recognition Directional basis probabilistic neural network |
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