摘要
针对模糊聚类神经网络FCNN(fuzzyclusteringneuralnetwork)对例外点(outliers)敏感的缺陷,通过引入Vapnik’sε-不敏感损失函数,重新构造网络的目标函数,并根据拉格朗日优化理论推导出新的鲁棒模糊聚类神经网络及其算法(robustfuzzyclusteringneuralnetworks,简称RFCNN).RFCNN有效地克服了FCNN对例外点敏感之缺点并且能得到合理的聚类中心.仿真实验结果表明,RFCNN较之于FCNN有更好的鲁棒性.
In this paper a new robust fuzzy clustering neural networks (RFCNN) is presented to resolve the sensitivity of the fuzzy clustering neural network (FCNN) to outliers in real datasets. The new objective function of RFCNN is obtained by introducing Vapnik's e-insensitive loss function, and RFCNN's update rules are derived by using Lagrange optimization theory. Compared with the FCNN algorithm, RFCNN is much more robust to outliers in the datasets. Experimental results demonstrate the effectiveness of RFCNN.
出处
《软件学报》
EI
CSCD
北大核心
2005年第8期1415-1422,共8页
Journal of Software
基金
No.60225015国家自然科学基金
No.BK2003017江苏省自然科学基金
No.NCET-0404962004年度国家教育部新世纪优秀人才计划
2005年度国家教育部科学研究重点项目~~