为有效提高矢量量化码书的性能和学习效率，需进一步改进自组织神经网络的学习算法。
Self-organizing neural network is a very efficient method for pattern recognition and vector quantization(VQ).
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.
对自组织特征映射神经网络的特性进行分析，并将其与矢量量化问题的实质进行比较，提出了一个实现矢量量化的自组织特征映射算法。
The characteristics of SOFM neural network is analysed and compared with the feature of Vector Quantizing problem in this paper. Based on this an algorithm for Vector Quantizing is put forward.
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