基于模糊模式识别的超圆神经网络模型

模糊模式识别是模糊集理论研究中的重要方向，神经网络是数据挖掘中的一种常用方
法。超圆神经网络的学习时间和网络模型理解性都优于BP 神经网络，它能以较少的数据量 蕴涵同样的信息量。文章依据超圆神经网络模型思想，提出了一种新的基于模糊模式识别的 神经网络模型算法，该算法继承了超圆神经网络的优点，能有效地对样本进行学习。
关健词：模糊集合论；数据挖掘；模糊模式识别；超圆神经网络

Research of the Algorithm of Artificial Neural Network based on Fuzzy Pattern Recognition
Liu Bing-xiang，Li Hai-lin，Li Hui-ying (School of Information Engineering, JDZ Ceramic Institute, JingDeZhen, 333001) Abstract: Fuzzy Pattern Recognition is an important research field on the fuzzy theory, Artificial neural network (ANN) is a common method of data mining. The learning period and network model understand of the super circle covering ANN are better than BP neural network. According to its thought, the new Artificial Neural Network based on fuzzy pattern recognition will be introduced. Comparing with the super circle covering ANN, this algorithm has the same excellent  aspect, in practice it is useful to learn the samples.
Key words: fuzzy sets；data mining；fuzzy pattern recognition；super circle covering artificial
neural network