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
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;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-2646-9
DOI :
10.1109/ISCID.2012.44
