On the basis of analyzing RBPNN in theory, subtractive clustering is used to determine its hidden centric vector.
在对径向基概率神经网络进行理论分析基础上，采用减法聚类方法确定它的隐中心矢量。
A novel graphic symbol recognition approach of engineering drawings based on radial basis probabilistic neural networks (RBPNN) is proposed.
基于径向基概率神经网络，提出一种扫描工程图纸图像分割后的图形符号识别方法。
Furthermore, on the basis of feature extraction, by utilizing the Radial basis Probabilistic Neural Networks (RBPNN), the palmprint recognition task could be implemented automatically.
在特征提取的基础上，进一步利用径向基概率神经网络(RBPNN)分类器，实现了掌纹的自动识别。
Furthermore, on the basis of feature extraction, by utilizing the Radial basis Probabilistic Neural Networks (RBPNN), the palmprint recognition task could be implemented automatically.
在特征提取的基础上，进一步利用径向基概率神经网络(RBPNN)分类器，实现了掌纹的自动识别。
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