Application of Self-Organizing Feature Map clustering to the classification of woodland communities
Artificial neural network is powerful in analyzing and solving complicated and non-linear matters. SOFM (self-organizing feature map) clustering was described and applied to the analysis of woodland communities in the Guancen Mountains of China. The dataset was consisted of importance values of 112 species in 53 quadrats. SOFM clustering classified the 53 quadrats into eight groups, representing eight associations of vegetation. These results are ecologically meaningful, which suggests that SOFM clustering is effective method in studies of ecology.
Artificial neural network quantitative method woodland classification
Jin-Tun Zhang Bo Sun Wenming Ru
College of Life Sciences Beijing Normal University Beijing 100875,China Department of Biology and Chemistry Changzhi University Changzhi 046011,China
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）