Predicting photosynthetic rate of sunflowers using back propagation neural network based on uniform design

	Predicting photosynthetic rate of sunflowers using back propagation neural network based on uniform design
	Chen, W. Y. ; Yang, J. ; Chen, Z. Y. ; Luo, F. Y. ; Wu, C. L. ; Peng, Z. S.
刊名	AFRICAN JOURNAL OF AGRICULTURAL RESEARCH
	2011
卷号	6 期号:27 页码:5817-5821
关键词	Back propagation neural network (BPNN) model photosynthetic rate (Pn) sunflower uniform design (UD) stepwise regression
ISSN号	1991-637X
通讯作者	Yang, J (reprint author), China W Normal Univ, Key Lab SW China Wildlife Resources Conservat, Nanchong 637009, Sichuan, Peoples R China.
产权排序	2
英文摘要	The new model to predict photosynthetic rate (Pn) using back propagation neural network (BPNN) based on uniform design (UD) was studied. Four parameters of BPNN were designed at six levels individually by UD experiment to optimize the architecture of the BPNN model. The optimal parameters were used to construct an intelligent, feasible BPNN model which could more accurately predict the photosynthetic rate of sunflowers response to environmental factors. The constructed BPNN model had three layers namely input layer, hidden layer with nine neurons and an output layer. Four environment factors including photosynthetic active radiation (PAR), temperature (T), carbon dioxide level (CO(2)) and relative humidity (RH) were input layers, and photosynthetic rate (Pn) as an output layer. Results showed that the predicted values and actual values of Pn fitted very well, with mean absolute percentage error (MAPE) of 3%, mean square error (MSE) of 0.75 mu mol CO(2) m(-2)s(-1) and mean absolute error (MAE) of 0.72 mu molCO(2) m(-2)s(-1). There was no significant difference using significant test between the actual values obtained from portable photosynthetic system and predicted value calculated by models. The conclusion was that the model established by BPNN based on UD was more accurate than stepwise regression to predict Pn of sunflowers giving the environmental factors (PAR, T, CO(2) and RH).
学科主题	Agriculture
收录类别	SCI
资助信息	Application and Basic Foundation; Science and Technology Bureau of Sichuan Province[2008JY0150]; Education Bureau of Sichuan Province[2003A098]; Sichuan Provincial Key Subject Program, People's Republic of China[SZD0420]
语种	英语
WOS记录号	WOS:000298505100003
公开日期	2012-11-09
内容类型	期刊论文
源URL	[http://210.75.237.14/handle/351003/23424]
专题	成都生物研究所_生态研究
推荐引用方式 GB/T 7714	Chen, W. Y.,Yang, J.,Chen, Z. Y.,et al. Predicting photosynthetic rate of sunflowers using back propagation neural network based on uniform design[J]. AFRICAN JOURNAL OF AGRICULTURAL RESEARCH,2011,6(27):5817-5821.
APA	Chen, W. Y.,Yang, J.,Chen, Z. Y.,Luo, F. Y.,Wu, C. L.,&Peng, Z. S..(2011).Predicting photosynthetic rate of sunflowers using back propagation neural network based on uniform design.AFRICAN JOURNAL OF AGRICULTURAL RESEARCH,6(27),5817-5821.
MLA	Chen, W. Y.,et al."Predicting photosynthetic rate of sunflowers using back propagation neural network based on uniform design".AFRICAN JOURNAL OF AGRICULTURAL RESEARCH 6.27(2011):5817-5821.