Rice Blast Prediction Based on Gray Ant Colony And RBF Neural Network Combination Model

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外文摘要：For rice blast gray system with complex nonlinearity， utilizing of gray ant colony GM(1， 1，θ) 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。

外文关键词：

rice blastgray systemgray ant colony predictionRBF neural network predictioncombination model

作者：

Liu Kun、Wang Zhiqiang

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作者单位：

College of Information Technology Heilongjiang Bayi Agricultural University Daqing, China

会议名称：

International Symposium on Computational Intelligence and Design

会议地点：

Hangzhou(CN)

会议母体文献：

2012 Fifth International Symposium on Computational Intelligence and Design

页码：

144-147

出版时间：

2012