摘要
针对竞争学习在给定的输出节点数目少于实际类数目时的学习结果会在几类数据之间振荡的问题,提出了M PTOC策略以及基于此策略的分裂-合并竞争学习算法.在假设数据集中的数据对其相应节点产生大小等于二者距离“吸引力”的基础上,算法通过计算网络中获胜节点在不同方向的“吸引力合力”分布,间接描述该节点附近数据的分布情况;采用高维空间模糊熵的方法确定该节点主要的“合力”方向,并将该节点在这几个方向上进行分裂-合并学习,从而实现M PTOC策略.通过对二维随机分布数据的实验结果验证了所提出算法的正确性和有效性.
An MPTOC strategy is presented, which guarantees each cluster has an output unit at least during learning. The concept of “attractive force” in mechanics is adopted to describe the relation between a unit and its corresponding data, which equal to their Euclid distance. Based on MPTOC splitting-merging competitive learning the distribution of data around their winning unit is estimatea indirectly through computing the unit attractive forces. And the unit splitting directions are assigned through method of fuzzy entropy in high dimension space. Then the unit is split and learned along these directions. To avoid over-segmentation of input dataset, the results of splittinglearning are merged with the help of their means and variances. Experiments in 2D space validate the proposed algorithm.
出处
《控制与决策》
EI
CSCD
北大核心
2005年第11期1229-1234,共6页
Control and Decision
基金
国家863计划项目(2F03H03
2F03H06)
关键词
竞争学习
分裂-合并竞争学习
MPTOC
模糊熵
Competitive learning
Splitting-merging competitive learning （SMCL）
MPTOC
Fuzzy entropy
