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。
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.
出处
《计算机工程》
CAS
CSCD
北大核心
2007年第5期180-181,184,共3页
Computer Engineering
基金
江苏省高校自然科学基金资助项目(05KJB520102)
扬州大学自然科学基金资助项目(KK0413160)
关键词
模糊聚类
神经网络集成
模式分类
Fuzzy clustering
Neural networks ensemble
Pattern classification
