DocumentCode :
527795
Title :
Modeling stand density index based on artificial neural network
Author :
Huang, Jiarong ; Gao, Guangqin ; Meng, Xianyu ; Guan, Yuxiu
Author_Institution :
Coll. of Forestry, Henan Agric. Univ., Zhengzhou, China
Volume :
4
fYear :
2010
fDate :
10-12 Aug. 2010
Firstpage :
1734
Lastpage :
1736
Abstract :
A stand density index BP model was created with artificial neural network modeling technology, by taking the average of diameter at breast height as the input variable, and stand number density as the output variable, in Masson pine planted forest. Through training and optimum seeking, the idea model structure is 1:2:1, the fitting accuracy is 98.67%. As a comparison, a Reineke stand density index model was created and fitted with regression analysis method and same sample data, the fitting accuracy is 97.76%. The results comparing with BP model and Reineke model indicate that the artificial neural network is a more effective stand density index modeling technique.
Keywords :
backpropagation; forestry; neural nets; regression analysis; BP model; Masson pine planted forest; Reineke stand density index model; artificial neural network modeling technology; regression analysis; Artificial neural networks; Biological system modeling; Data models; Fitting; Indexes; Mathematical model; Neurons; Pinus massoniana; artificial neural network; model; stand density index;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
Type :
conf
DOI :
10.1109/ICNC.2010.5584350
Filename :
5584350
Link To Document :
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