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

共 50 条

[31] An image segmentation approachbased on self-organizing feature map and GLVQ
Xia Hui
Mu Xihui
Ma Zhenshu
Du Fengpo
Lan Jian
Proceedings of the First International Symposium on Test Automation & Instrumentation, Vols 1 - 3, 2006, : 479 - 482
[32] New Dynamic Self-Organizing Feature Maps for the Classification of Extracted Feature Vectors of Characters
Benny, Dayana
Soumya, Kumary R.
Rao, K. Nageswara
2015 INTERNATIONAL CONFERENCE ON ROBOTICS, AUTOMATION, CONTROL AND EMBEDDED SYSTEMS (RACE), 2015,
[33] Grid topologies for the self-organizing map
Lopez-Rubio, Ezequiel
Diaz Ramos, Antonio
NEURAL NETWORKS, 2014, 56 : 35 - 48
[34] Self-organizing feature map neural network classification of the ASTER data based on wavelet fusion
HASI Bagan
ScienceinChina(SeriesD:EarthSciences), 2004, (07) : 651 - 658
[35] Application of self-organizing map to stellar spectral classifications
Bazarghan, Mahdi
ASTROPHYSICS AND SPACE SCIENCE, 2012, 337 (01) : 93 - 98
[36] Application of self-organizing map to stellar spectral classifications
Mahdi Bazarghan
Astrophysics and Space Science, 2012, 337 : 93 - 98
[37] Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles
Wunsch, Andreas
Liesch, Tanja
Broda, Stefan
WATER RESOURCES MANAGEMENT, 2022, 36 (01) : 39 - 54
[38] Classification of Protein Sequences using the Growing Self-Organizing Map
Ahmad, Norashikin
Alahakoon, Damminda
Chau, Rowena
2008 4TH INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION FOR SUSTAINABILITY (ICIAFS), 2008, : 287 - +
[39] Self-organizing map for the classification of normal and disordered female voices
Callan, DE
Kent, RD
Roy, N
Tasko, SM
JOURNAL OF SPEECH LANGUAGE AND HEARING RESEARCH, 1999, 42 (02): : 355 - 366
[40] Genetically Supervised Self-Organizing Map for the Classification of Glass Samples
de Groof, Richard
Valova, Iren
2014 13TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2014, : 251 - 256

← 1 2 3 4 5 →