DocumentCode
589399
Title
Rice Blast Prediction Based on Gray Ant Colony and RBF Neural Network Combination Model
Author
Liu Kun ; Wang Zhiqiang
Author_Institution
Coll. of Inf. Technol., Heilongjiang Bayi Agric. Univ., Daqing, China
Volume
1
fYear
2012
fDate
28-29 Oct. 2012
Firstpage
144
Lastpage
147
Abstract
For rice blast gray system with complex nonlinearity, utilizing of gray ant colony model and RBF neural network model characteristics, gray ant colony and RBF neural network combination model is presented in this paper. After 10 years (2002-2011) prediction analysis of rice blast, the prediction accuracy of this project is up to 97.84%, and verifies the validity of the prediction model.
Keywords
agriculture; ant colony optimisation; crops; diseases; grey systems; neural nets; radial basis function networks; RBF neural network combination model; RBF neural network model characteristics; complex nonlinearity; gray ant colony model; prediction analysis; rice blast gray system; rice blast prediction; Accuracy; Analytical models; Educational institutions; Mathematical model; Neural networks; Predictive models; Vectors; RBF neural network prediction; combination model; gray ant colony prediction; gray system; rice blast;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on
Conference_Location
Hangzhou
Print_ISBN
978-1-4673-2646-9
Type
conf
DOI
10.1109/ISCID.2012.44
Filename
6406939
Link To Document