Application of Self-Organizing Feature Map clustering to the classification of woodland communities

被引：0

作者：

Zhang, Jin-Tun ^{[1
]}

Sun, Bo ^{[1
]}

Ru, Wenming ^{[2
]}

机构：

[1] Beijing Normal Univ, Coll Life Sci, Beijing 100875, Peoples R China

[2] Changzhi Univ, Dept Biol Chem, Changzhi 046011, Peoples R China

来源：

2009 3RD INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING, VOLS 1-11 | 2009年

基金：

中国国家自然科学基金;

关键词：

Artificial neural network; quantitative method; woodland; classification; NEURAL-NETWORK; LOESS PLATEAU; VEGETATION; CLIMATE; SOM;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页码：3080 / +

页数：2

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