Furao Shen, Ogura Tomotaka, Hasegawa Osamu
The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, PR China.
Neural Netw. 2007 Oct;20(8):893-903. doi: 10.1016/j.neunet.2007.07.008. Epub 2007 Aug 14.
An enhanced self-organizing incremental neural network (ESOINN) is proposed to accomplish online unsupervised learning tasks. It improves the self-organizing incremental neural network (SOINN) [Shen, F., Hasegawa, O. (2006a). An incremental network for on-line unsupervised classification and topology learning. Neural Networks, 19, 90-106] in the following respects: (1) it adopts a single-layer network to take the place of the two-layer network structure of SOINN; (2) it separates clusters with high-density overlap; (3) it uses fewer parameters than SOINN; and (4) it is more stable than SOINN. The experiments for both the artificial dataset and the real-world dataset also show that ESOINN works better than SOINN.
提出了一种增强型自组织增量神经网络(ESOINN)来完成在线无监督学习任务。它在以下方面改进了自组织增量神经网络(SOINN)[Shen, F., Hasegawa, O. (2006a). An incremental network for on-line unsupervised classification and topology learning. Neural Networks, 19, 90 - 106]:(1)采用单层网络代替SOINN的双层网络结构;(2)分离高密度重叠的聚类;(3)使用比SOINN更少的参数;(4)比SOINN更稳定。人工数据集和真实世界数据集的实验也表明ESOINN比SOINN表现更好。