文中探讨了一种用于提取模糊规则的RBF神经网络结构，提出了基于此网路结构的模糊隶属度函数学习算法，最后给出了用于验证该算法有效性的仿真实例。

The learning algorithm of membership function based on the RBF Neural Network is discussed and an example is given to demonstrate the validity of this algorithm.

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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.

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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。

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.

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