摘要： 模糊规则在许多领域中得到了非常成功的应用,但是要从大量的数据资料中获取适当的模糊规则和隶属度函数却是一件困难的事情。而神经网络的并行计算可以快速高效地处理数据资料,找出其规律性,但是模糊规则如何在神经网络中表示呢?本文提出将神经网络应用于提取模糊规则和调整其隶属度函数中,详细阐述了该神经网络的模型及其学习算法和模糊规则在神经网络中的表示。

关键词: 知识获取, 模糊规则, 隶属度函数, 神经网络

Abstract: Fuzzy rules have been succeeded in many fields. But it is difficult to extract fuzzy rules and get cheir membership functions from massive data information.Ncural network can deal with massive data information parallelly and find out their regulation. But how to represent knowledges in neural network? This paper suggests artificial neural network used to extract fuzzy rules and adjust their membership functions.It explains the model of neural network, learning algorithms and the knowledge representation in neural network in detail.

中图分类号: 

• TP183