摘要： 基于模糊聚类思想，提出了一种神经网络集成方法。利用隶属度函数，构造了一个分布函数，根据分布函数对训练数据进行抽样，用所抽得的数据作为个体神经网络的训练样本，多个个体神经网络构成神经网络集成，集成的输出采用相对多数投票法。理论分析和实验结果表明，该方法对模式分类能取得较好的效果。

关键词: 模糊聚类, 神经网络集成, 模式分类

Abstract: Based on fuzzy clustering, a method for neural network ensemble is proposed. Using membership function, a distributed function is constructed and based on it, data are sampled from training samples. Then these data are used as training set of individual neural networks, many individual neural networks constitute neural network ensemble and the output of the ensemble uses majority voting method. Theoretical analysis and experimental results show that this neural network ensemble method is efficient for pattern classification.

Key words: Fuzzy clustering, Neural networks ensemble, Pattern classification