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1·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.

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)。

2·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.

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。

3·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。

4·The learning vector quantization neural network has been integrated successfully with the case-based reasoning approach to reduce the case indexing space and to enhance the indexing efficiency.

将学习矢量量化神经网络集成在基于实例推理的故障诊断方法中，减小了实例搜索空间，提高了实例检索效率。

5·Objective: to investigate the potential of learning vector quantization (LVQ) artificial neural network tools for discrimination and forecasting of occurrent intensity of typhoid and paratyphoid.

目的：探讨学习矢量量化(LVQ)人工神经网络在伤寒、副伤寒发生强度判别与预测中的应用。

6·Neural network competitive learning algorithms are widely used for vector quantization. In this paper, some typical competitive learning algorithms have been specially investigated and analyzed .

对典型的竞争学习算法进行了研究和分析，提出了一种基于神经元获胜概率的概率敏感竞争学习算法（PSCL）。