• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2008, Vol. 30 ›› Issue (6): 76-78.

• 论文 • 上一篇    下一篇

基于统计检验的模糊聚类神经网络

吴斯 程玉林 张诚坚   

  • 出版日期:2008-06-01 发布日期:2010-05-19

  • Online:2008-06-01 Published:2010-05-19

摘要:

针对模糊聚类神经网络FCNN原有学习算法对初值敏感性、吸引域不灵活和稳定点不合理等局限性。本文提出基于统计检验的模糊聚类神经网络FCNN-ST。通过引入T平方抽样的单峰分布模式统计检验逐步调整网络结构。确定最佳聚类数c。并使算法的稳定点趋于合理的聚类中心。仿真结果表明。FCNN-ST具有较好的鲁棒性。

关键词: 模糊聚类 神经网络 统计检验

Abstract:

In this paper, a fuzzy clustering neural network based on statistical tests (FCNN-ST) is presented to resolve the weaknesses of the current learning algorithm of fuzzy clustering neural networks (FCNN), such as initial condition sensitiveness, inflexible attraction domain and unreasonable converge ence points. Statistical tests of the unimodal distribution model based on the T-square sample are introduced to gradually adjust the structure of netwo rks and determine the optimal number of dusters. The results of simulation demonstrate the effectiveness of FCNN-ST.

Key words: fuzzy clustering, neural network, statistical test