针对汽轮机转子故障分类问题，采用模糊数学和自组织特征映射神经网络方法诊断汽轮机转子的故障。
In the light of the problems involved in a steam turbine rotor fault diagnosis proposed in this paper is a new diagnostic method based on a fuzzy self organizing neural network.
提出曲元分析(CCA)和自组织特征映射(SOFM)相结合的方法用于轴承的故障诊断特征提取。
The combination of curvilinear component analysis (CCA) and self-organizing feature map (SOFM) were applied to a diagnosis for fault feature extraction of bearing.
本文应用多层前馈神经网络和自组织特征映射神经网络分别对简单目标和复杂飞机目标进行了分类识别。
The classification of simple and complex objects is investigated using the multiple layer forward neural network and the self-organizing feature map network.
本文研究了多层感知器、径向基函数网络、学习向量量化网络和自组织特征映射网络等四种神经网络在回转窑火焰图像分割中的应用。
In this paper, four neural networks, i. e. multi layer perception, radial basis function, learning vector quantization and self organizing feature mapping, are used to segment the flame image.
本文研究了多层感知器、径向基函数网络、学习向量量化网络和自组织特征映射网络等四种神经网络在回转窑火焰图像分割中的应用。
In this paper, four neural networks, i. e. multi layer perception, radial basis function, learning vector quantization and self organizing feature mapping, are used to segment the flame image.
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