本文分析了模糊矢量量化(FVQ)图像编码的原理，提出了一种指数型模糊学习矢量量化算法(EFLVQ)。
The principle of fuzzy vector quantization (FVQ) for image coding is discussed in this paper, and an exponential fuzzy learning vector quantization algorithm (EFLVQ) is proposed.
仍然有些算法很容易就可以被归入好几个类别，好比学习矢量量化，它既是受启发于神经网络的方法，又是基于实例的方法。
There are still algorithms that could just as easily fit into multiple categories like Learning Vector Quantization that is both a neural network inspired method and an instance-based method.
对向传播神经网络(CPN)可以作为矢量量化器用于图像压缩，但CPN学习算法在进行码书设计时存在两个明显的缺陷。
The Counterpropagation Network (CPN) can be applied to image compression as a vector quantizer. However, the CPN learning algorithm has two obvious disadvantages in codebook designing.
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).
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).
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