基于无监督学习算法训练径向基概率神经网络 TRAINING THE RADIAL BASIS PROBABILISTIC NEURAL NETWORK BASED ON A UNSUPERVISED LEARNING ALGORITHM ON A UNSUPERVISED LEARNING ALGORITHM 下载PDF阅读器 本文研究了径向基概率神经网络(Radial Basis Probabilistic Neural Networks,RBPNN)的一种新的无监督学习算法,该算法整合了径向基概率神经网络的结构原理与动态聚类算法的特点,使得在对训练
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...VISED LEARNING ALGORITHM 下载PDF阅读器 本文研究了径向基概率神经网络(Radial Basis Probabilistic Neural Networks,RBPNN)的一种新的无监督学习算法,该算法整合了径向基概率神经网络的结构原理与动态聚类算法的特点,使得在对训练..
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使用递归正交最小二乘算法(ROLSA)优选径向基概率神经网络(RBPNN)的隐中心矢量,微遗传算法(μGA)用于求解RBPNN最优核函数控制参数,并同ROLSA相结合(ROLS-μGA)来优化RBPNN的全结构(优选最优控制参...
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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。
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